@agentled/mcp-server

The automation engine built for AI agents. Intelligent AI workflow orchestration with long-term memory, 100+ integrations, and unified credits.

What is Agentled?

Agentled is the automation engine built for AI agents. It gives Claude, Codex, Cursor, Windsurf, and any MCP-compatible client direct access to intelligent workflow orchestration, long-term memory, and 100+ integrations.

Three things make it different:

🧠 Long-Term Memory — A built-in Knowledge Graph stores insights across workflow executions. Your agents get smarter over time — they remember past research, lead scores, content performance, and business context.

⚡ Unified Credits — One API key, one credit system, 100+ services. No need to sign up for LinkedIn, email, scraping, AI models, or video generation separately. Connect once, use everything.

🎯 Intelligent Orchestration — AI reasons at every step. Workflows aren't just "if this then that" — they understand context, make decisions, and adapt to results.

See it in action

$ agentled create "Outbound to fintech CTOs in Europe"

Loading workspace context from Knowledge Graph...
✦ ICP loaded  ✦ 3 prior campaigns  ✦ 847 contacts in KG

Creating campaign with 3 workflows...

━━ Workflow 1: Prospect Research  linkedin · hunter · clearbit
  ✓ LinkedIn: CTO + fintech + EU → 189 profiles
  ✓ Enriched via Hunter + Clearbit → 156 matched
  ✓ ICP scoring → 43 high-intent leads

━━ Workflow 2: Signal Detection  web-scraper · crunchbase
  ✓ Job postings → 12 hiring devops
  ✓ Crunchbase → 8 recently funded
  ✓ Cross-match: hiring + funded → 5 hot leads

━━ Workflow 3: Outreach  email · linkedin · kg
  ✓ Personalized emails from context
  ✓ LinkedIn requests with custom notes
  ✓ 43 leads saved to Knowledge Graph

Campaign saved. Scheduled: every 48h
Credits used: 720
→ https://www.agentled.app/your-team/fintech-cto-outbound

One prompt. Three workflows. LinkedIn enrichment, email finding, AI scoring, multi-channel outreach — all orchestrated, all stored in the Knowledge Graph for the next run.

Quick Start

claude mcp add --transport stdio --scope user agentled \
  -e AGENTLED_API_KEY=wsk_... \
  -- npx -y @agentled/mcp-server

--scope user registers the server in your user MCP config so it loads in every project (not only the repo where you ran the command). Use a distinct server name (e.g. agentled_my_workspace) if you add multiple workspaces. For team-shared config in git, use --scope project and .mcp.json instead (Claude Code MCP scopes).

Claude Code plugin (one-step install)

Prefer the plugin if you want the MCP server and the Agentled skill installed together. In Claude Code:

/plugin marketplace add Agentled/mcp-server
/plugin install agentled@agentled

Then set your API key in the shell Claude Code runs from:

export AGENTLED_API_KEY=wsk_...

The plugin bundles the agentled skill (workflow-authoring guidance, namespaced agentled:agentled) and auto-starts the MCP server via npx -y @agentled/mcp-server. The same plugin directory also carries the Codex manifest (.codex-plugin/) — one bundle, both hosts.

Pick one install path, not both. If you previously ran claude mcp add agentled ... or --setup-skills, remove those before (or instead of) installing the plugin — otherwise you get two identical MCP server processes and the skill registered twice. Cleanup: claude mcp remove agentled and delete .claude/skills/agentled/ (or ~/.claude/skills/agentled/). --setup-skills now detects an installed plugin and refuses to double-register unless you pass --force.

To develop the plugin locally:

claude --plugin-dir ./plugins/agentled     # load from source
claude plugin validate ./plugins/agentled  # check manifest + structure

plugins/agentled/skills/ is a generated mirror of skills/ (synced by publish.sh) — edit skills/agentled/SKILL.md, never the mirror.

Local development

Use the local built entrypoint when you want to test unpublished changes against a local app. npx -y @agentled/mcp-server always uses the latest published npm package.

cd agentled-mcp-server
npm run build

claude mcp add --transport stdio agentled_local \
  --env AGENTLED_API_KEY=wsk_... \
  --env AGENTLED_URL=http://localhost:8080 \
  -- node /absolute/path/to/agentsled-front/agentled-mcp-server/dist/index.js

Getting your API key

1. Sign up at agentled.app
2. Open Workspace Settings > Developer
3. Generate a new API key (starts with wsk_)

Why Agentled MCP?

One API Key. One Credit System. 100+ Services.

No need to sign up for LinkedIn APIs, email services, web scrapers, video generators, or AI models separately. Agentled handles all integrations through a single credit system.

Capability	Credits	Without Agentled
LinkedIn company enrichment	50	LinkedIn API ($99/mo+)
Email finding & verification	5	Hunter.io ($49/mo)
AI analysis (Claude/GPT/Gemini)	10-30	Multiple API keys + billing
Web scraping	3-10	Apify account ($49/mo+)
Image generation	30	DALL-E/Midjourney subscription
Video generation (8s scene)	300	RunwayML ($15/mo+)
Text-to-speech	60	ElevenLabs ($22/mo+)
Knowledge Graph storage	1-2	Custom infrastructure
CRM sync (Affinity, HubSpot)	5-10	CRM API + middleware

Workflows That Learn

Other automation tools start from zero every run. Agentled's Knowledge Graph remembers across executions — what worked, what didn't, what humans corrected. Scoring workflows can use compact row-level scoring_profile summaries and bounded scoring-memory retrieval so every run compounds on the last without dumping raw history into prompts.

Run 1:  Investor scoring → 62% accuracy (cold start)
Run 5:  → 78% (learning from IC feedback)
Run 12: → 89% (compound learning from outcomes, zero manual tuning)

Intelligent Orchestration

Unlike trigger-action tools, Agentled workflows have AI reasoning at every step. Multi-model support (Claude, GPT-4, Gemini, Mistral, DeepSeek, Moonshot), adaptive execution, and human-in-the-loop approval gates when needed.

Agent Teams

Agent Teams let you run multiple AI specialists in a single workflow step. Pick a preset and describe what you need — the team handles coordination, delegation, and synthesis.

"Add an Agent Team step that researches the company and produces an investment memo"

Six built-in presets cover the most common patterns:

Preset	What it does
research-and-summarize	Specialists gather information, one synthesizes a summary
analyze-and-recommend	Multiple analysts evaluate options, produce a ranked recommendation
generate-then-review	A generator drafts content, reviewers critique and refine
compare-options	Specialists argue for competing options, coordinator arbitrates
investigate-in-parallel	Independent specialists explore different angles simultaneously
review-and-improve	Reviewers find issues, an editor applies improvements

When creating Agent Team steps via MCP, include preset metadata so the step opens correctly in the builder:

{
  "id": "analyze",
  "type": "agentOrchestrator",
  "name": "Agent Team",
  "orchestratorConfig": {
    "pattern": "supervisor",
    "workers": [
      { "id": "researcher", "name": "Researcher", "systemPrompt": "Research {{input.company_url}} — team, funding, market position" },
      { "id": "analyst", "name": "Analyst", "systemPrompt": "Analyse the research. Identify risks and growth signals." }
    ]
  },
  "metadata": {
    "agentTeamPreset": "research-and-summarize",
    "agentTeamMode": "simple",
    "agentTeamUxVersion": 1
  },
  "next": { "stepId": "milestone" }
}

Existing steps created with raw orchestratorConfig and no metadata continue to work — they open in advanced mode in the builder without errors.

Analytics vs ROI semantics

When describing workflow outcomes, keep these terms separate:

• pipeline.analyticsConfig = business metrics (execution outcome stats shown in Business Metrics cards/charts).
• pipeline.metadata.roi = ROI assumptions/rollups (time saved and cost-value estimates).

If you update one without the other, name exactly what changed (e.g. "business metrics configured" vs "ROI assumptions configured").

CLI parity guard

The repository includes an automated parity guard so MCP tool additions do not silently drift from the CLI surface.

• Test: __tests__/cli/cli-mcp-parity.test.ts
• Docs: docs/CLI_MCP_PARITY.md

Run it with:

yarn test:node -- cli-mcp-parity.test.ts

What Can You Build?

Lead Enrichment & Sales Automation

"Find fintech CTOs in Europe, enrich via LinkedIn + Hunter, score by ICP fit,
draft personalized outreach, save everything to the Knowledge Graph"

Content & Media Production

"Scrape trending topics in our niche, generate 5 LinkedIn posts with AI,
create thumbnail images, schedule publishing for the week"

Company Research & Intelligence

"Research this company from its URL — team, funding, market position, competitors.
Generate an investment memo. Store in KG for future reference."

VC Investor Matching (real case study)

"Match this startup against our 2,000+ investor database. Score by sector focus,
stage preference, check size, and portfolio synergy. Compare with last round's outcomes."

3,000+ profiles processed. IC-ready reports. Prediction vs outcome learning — accuracy went from 62% to 89% over 12 runs with zero manual tuning.

Built-in Skills And Integrations

Media Production: Video generation, image generation, text-to-speech, auto-captions, media assembly

AI Intelligence: Multi-model AI (Claude, GPT-4, Gemini, Mistral, DeepSeek, Moonshot, xAI), Knowledge Graph, feedback loops, scoring & analytics

Data & Integration: LinkedIn (search, enrich, post), email (send, personalize), web scraping, social publishing, CRM sync, document analysis, OCR

Available Tools

Workflows

Tool	Description
list_workflows	List all workflows in the workspace
get_workflow	Get full workflow definition by ID
get_workflow_credits	Get ledger-derived, period-labelled workflow credit usage; opt in to cost drivers with includeCostDrivers
create_workflow	Create a new workflow from pipeline JSON
update_workflow	Update an existing workflow (top-level scalars; for context/metadata prefer update_workflow_context)
update_workflow_context	Workflow-level analog of update_step — three explicit verbs (updates / replace / unset) on context.* and metadata.* paths, returns diff + warnings
add_step	Add a step with automatic positioning and next-pointer rewiring
update_step	Deep-merge updates into a single step by ID
remove_step	Remove a step with automatic next-pointer rewiring
delete_workflow	Permanently delete a workflow
validate_workflow	Validate pipeline structure, returns errors per step
publish_workflow	Change workflow status (draft, live, paused, archived)
export_workflow	Export a workflow as portable JSON
import_workflow	Import a workflow from exported JSON

Public Form Links

Public form links are the external intake surface for workflows with context.executionInputConfig fields. Use them when people outside the workspace need to submit a workflow form without signing in: inbound lead forms, pitch deck submissions, referral forms, support intake, assessment questionnaires, or any workflow whose first step is a manual/input trigger.

Do not use a public form link for internal child workflows. Child workflows should use context.executionInputConfig.internal: true and be called from another workflow with agentled.call-workflow.

Tool	Description
list_public_form_links	List existing public form links for a workflow
create_public_form_link	Create and enable a public form link
update_public_form_link	Enable/disable a link or update limits, expiry, auto-share, and thank-you copy. To revoke external access, set enabled: false.

Deletion is intentionally not exposed via the external API or MCP. To revoke a public form link, call update_public_form_link with enabled: false. Permanent deletion requires an authenticated workspace member acting through the UI — destructive ops on the form-link surface are not granted to the public API key.

Typical agent flow:

1. get_workflow({ workflowId })
2. Confirm context.executionInputConfig exists and is not internal.
3. list_public_form_links({ workflowId })
4. If none exists, create_public_form_link({ workflowId, enabled: true })
5. Return the publicUrl to the user.

The public URL is /en/forms/{formLinkId}. On submit, Agentled validates the form link, starts the workflow with the submitted input, records a PublicFormSubmission, and increments submissionCount. Optional settings:

• enabled: disable without deleting the link.
• expiresAt: ISO datetime expiry.
• submissionLimit: maximum accepted submissions.
• autoShare: when true, the public form status page can show generated results after completion. Use this only when the workflow output is safe for the submitter to see.
• shareExpiresInDays: expiry for auto-shared result links.
• successMessage: custom thank-you message after submission.

Internal-only Workflows

Mark a workflow as a child / sub-workflow that is only run via agentled.call-workflow from an orchestrator by setting context.executionInputConfig.internal: true. The UI then hides the Run button and replaces the manual run form with an info banner. Inputs are still validated and passed by orchestrators via executionInputData exactly as before — this is a UI guard, not a runtime restriction.

Use it for any workflow whose goal/description starts with "Internal sub-workflow", that ends in a return step, or that you only intend to invoke from another workflow.

{
  "context": {
    "executionInputConfig": {
      "title": "Save Sourced Candidates",
      "internal": true,
      "fields": [{ "name": "candidates", "label": "Candidates", "type": "text", "required": true }]
    }
  }
}

Flip the flag via update_workflow_context — fetch first, merge locally, replace at the parent level (the merge-order trap from update_step applies here too — see docs/MCP_STEP_EDITING.md):

// 1. get_workflow → read context.executionInputConfig
// 2. local: { ...executionInputConfig, internal: true }
// 3.
{
  "updates": { "context": { "executionInputConfig": {...full merged value...} } },
  "replace": ["context.executionInputConfig"]
}

Editing existing workflows: merge model

update_step accepts three explicit operations on the same call. At least one must be non-empty.

• updates — partial step patch, deep-merged ONE LEVEL deep. Top-level scalars are replaced; nested objects (pipelineStepPrompt, stepInputData, etc.) get their direct keys merged with the stored value's keys. Keys nested two levels deep are overwritten as a unit, not merged.
• replace: string[] — dot-paths whose values from updates are assigned wholesale, skipping the deep-merge. Use this for dictionary-shaped fields where keys are user data (not config) — patching one inner key with updates alone silently wipes the others.
• unset: string[] — dot-paths to delete. Each path must currently exist on the step (validated against the original).

Read before editing dictionary fields. Before changing stepInputData.fieldUpdates, pipelineStepPrompt.responseStructure, knowledgeSync.fieldMapping, or any field where keys are user data: call get_step({ workflowId, stepId }) (~1KB), modify locally, send the full new object back via replace[]. This avoids the "patched one key, silently wiped the others" trap.

Diff in the response. Every update_step call returns diff: { addedPaths, changedPaths, removedPaths } and warnings[]. If the merge silently removed ≥6 fields without an explicit unset, a warning fires.

What to use where:

Path / field	API	How to edit	Notes
name, goal, description, pipelineStepPrompt.template, creditCost	update_step	updates	Plain scalar; safe to send alone.
next, loopConfig, entryConditions (full block)	update_step	updates	Direct nested config; sending the new value wholesale is fine.
tools, integrations	update_step	updates	Arrays replace wholesale by design. To append, fetch with get_step, splice locally, send the full new array.
stepInputData.fieldUpdates	update_step	get_step → updates (full dict) + replace: ["stepInputData.fieldUpdates"]	Keys are user data; default one-level merge replaces this dict and can drop sibling mappings.
pipelineStepPrompt.responseStructure	update_step	get_step → updates + replace: ["pipelineStepPrompt.responseStructure"]	Output-shape dictionary; treat as user data.
knowledgeSync.fieldMapping	update_step	get_step → updates + replace: ["knowledgeSync.fieldMapping"]	Source→target dict; same trap as fieldUpdates.
renderer.config (when preserving sibling keys matters)	update_step	updates (full renderer.config) + replace: ["renderer.config"]	⚠ replace: ["renderer.config.layout"] does NOT protect renderer.config's siblings — one-level deep-merge runs first on updates.renderer. Replace at the parent level.
entryConditions.criteria (when preserving the rest of entryConditions)	update_step	updates: { entryConditions: {...full block...} }	Send the full entryConditions block; one-level merge already does the right thing for direct children.
Removing a step input or stale field	update_step	unset: ["stepInputData.oldKey"]	Cleanest way to remove. Path must exist on the original.
context.inputPages, context.outputPages, context.executionInputConfig	update_workflow_context	Three explicit verbs (updates / replace / unset) on workflow-relative paths. Compatibility: { contextKey, value } still accepted for wholesale per-key replacement.	Workflow-level, not step-level. update_step cannot reach context.* and vice versa.
metadata	update_workflow_context	Same three verbs on metadata.* paths	Workflow-level. Metadata bypasses the draft snapshot — even on live workflows it writes direct to the Pipeline row, immediately.

Executive summaries for workflow groups. When a user asks to save a summary for a workflow, workflow group, cluster, or home card, write it to metadata.executiveSummary with update_workflow_context. Do not store it as Knowledge text unless the user explicitly asks for a reusable note. For a group, write exactly once to the owner pipeline: prefer metadata.workflowGraph.role === "orchestrator", otherwise use the lowest metadata.workflowGraph.order pipeline. Keep the body to 1-2 short sentences, include concrete counts/rates and the reporting period when available, and set author to the active workspace agent, not the external tool/coding agent.

{
  "workflowId": "2e1cdd60-2fcf-441a-856f-583ae76b38a5",
  "updates": {
    "metadata": {
      "executiveSummary": {
        "body": "Startup Outreach sent 46 founder emails for the reporting period, with 28 opens and 9 clicks: a 60.9% open rate, 19.6% click rate, and 32.1% click-to-open rate.",
        "bullets": ["Clicks: 6 UTM Pitch Night, 2 plain Pitch Night, 1 calendar."],
        "generatedAt": "2026-06-03T00:00:00.000Z",
        "author": "AngelHive Assistant"
      }
    }
  }
}

Type changes. step.type is technically mutable but stale type-specific fields (pipelineStepPrompt, app, tools, orchestratorConfig) persist unless you unset them. For clean conversions, prefer remove_step + add_step.

Live workflows. Edits are routed to a draft snapshot. Response includes editingDraft: true. Inspect via get_draft, ship via promote_draft, throw away via discard_draft. For high-stakes edits, create_snapshot first as a manual checkpoint.

Draft staleness. When a draft exists, every update_step and get_step response includes a draft summary with exists, draftCreatedAt, liveUpdatedAt, stale, modifiedStepIds, and modifiedFields. If draft.stale === true, the live workflow advanced after the draft was created — promoting will land the draft's older values for fields you didn't touch. update_step also emits a staleness warning. Recovery: discard_draft and re-apply.

⚠ discard_draft only reverts pending context (and step) changes — NOT metadata. Metadata writes via update_workflow_context bypass the draft and apply immediately to the live Pipeline row. If you need a single rollback point covering metadata too, create_snapshot before the edit. See docs/MCP_STEP_EDITING.md for the full atomicity contract.

Never send a full steps[] array via update_workflow. Use update_step, add_step, remove_step instead.

For the deep reference (StepMergeError codes, dot-path validation rules, full diff semantics) see docs/MCP_STEP_EDITING.md.

Drafts & Snapshots

Tool	Description
get_draft	Get the current draft version of a workflow
promote_draft	Promote a draft to the live version
discard_draft	Discard the current draft
create_snapshot	Create a manual config snapshot
delete_snapshot	Delete a specific config snapshot
list_snapshots	List version snapshots for a workflow
get_snapshot_content	Read a snapshot's full config (steps, context, etc.) without restoring it
restore_snapshot	Restore a workflow to a previous snapshot

Executions

Tool	Description
start_workflow	Start a workflow execution with input. Returns executionInputId always and executionId only when the async execution row is already available. Pass useMocks: false to force a real (credit-consuming) run that ignores per-step mock data; defaults to honoring the workflow's configured mocks.
list_executions	List executions for a workflow (paginated via nextToken), including pipelineExecutionInputId for matching a start result that only returned executionInputId.
get_execution	Get execution details with step results. Requires the real executionId, not executionInputId; if needed, call list_executions and match pipelineExecutionInputId.
list_timelines	List step execution records (timelines) for an execution (paginated via nextToken)
get_timeline	Get a single timeline by ID with full step output
stop_execution	Stop a running execution
retry_execution	Retry a failed step — auto-detects the most recent failure if no timeline ID provided
rerun	Rerun or retry any step by timelineId — works for failed AND succeeded steps, disambiguates loop iterations

Run deep links use /<locale>/<workspace>/<workflowPathname>/runs?runId=<executionId>&step=<stepId>. The step query param is optional; when present, the app expands that workflow step and scrolls to it. stepId is the workflow step id, not the timeline id. Inside the app chat/navigation tool surface, navigateToExecutionPage accepts the same optional stepId.

Knowledge row deep links use /<locale>/<workspace>/knowledge-and-data/<listKey>?rowId=<rowId>. The app opens the Knowledge & Data list page and opens the row editor sheet for that row. The backwards-compatible route /<locale>/<workspace>/knowledge-and-data/<listKey>/row/<rowId> redirects to the canonical query-param URL.

Knowledge text deep links use /<locale>/<workspace>/knowledge-and-data/<key>. The backwards-compatible route /<locale>/<workspace>/knowledge-and-data/text/<key> redirects to the canonical item URL.

Apps & Testing

Tool	Description
list_apps	List available apps and integrations
get_app_actions	Get action schemas for an app
test_app_action	Test an app action without creating a workflow
test_ai_action	Test an AI prompt without creating a workflow
test_code_action	Test JavaScript code in the same sandboxed VM as production
get_step_schema	Get allowed PipelineStep fields grouped by category

AI step types: aiAction vs aiActionWithTools

Pick the right type — validate_workflow will reject the wrong one:

You need…	Use
Reason over inputs already present in the prompt variables	aiAction (single LLM call, no tool loop)
Live web search, workspace memory recall/write, knowledge-graph lookup	aiActionWithTools with the matching builtinType
The AI to decide at runtime what inputs to pass to an app action	aiActionWithTools with an appActionConfig tool

aiActionWithTools requires at least one tool — placed under step.tools or step.agent.tools (both are merged at runtime). If you omit tools from both locations, validate_workflow returns a blocker AI_STEP_TOOLS_REQUIRED. If the prompt says "search the web" / "recall memory" / "knowledge graph" without the matching tool attached, you get a warning AI_STEP_TOOL_PROMPT_MISMATCH: web-search prompts need web_search; memory prompts need workspace_memory; KG lookup prompts need kg_search or kg_traverse. fetch_website_content fetches a known URL and kg_write writes KG data, so neither satisfies those lookup/search prompts.

Valid builtinType values: web_search, file_search, code_interpreter, fetch_website_content, kg_search, kg_traverse, kg_nodes, kg_write, workspace_memory.

Prompt caching for repeated AI steps

Agentled enables provider prompt caching where supported. Caching reuses prompt processing for an identical prefix; it does not cache or replay the AI response. To benefit in bulk workflows, especially scoring/matching loops, write prompts with the stable material first:

[stable role and task]
[stable rubric / scoring dimensions]
[stable output JSON schema]
[stable examples or decision rules]

Runtime inputs:
{{currentItem}}
{{steps.previous.output}}
{{input.field}}

Do not start high-volume prompts with INPUTS, {{currentItem}}, {{steps.*}}, {{input.*}}, {{execution.id}}, {{now}}, or {{today}}. Keep changing payloads, dates, execution IDs, and per-item records at the end so OpenAI/Anthropic can cache the shared prefix.

// aiActionWithTools example
{
  "id": "research",
  "type": "aiActionWithTools",
  "name": "Research Company",
  "tools": [
    { "type": "builtin", "builtinType": "web_search", "name": "Web Search" }
  ],
  "pipelineStepPrompt": {
    "template": "Search the web for the founder of {{input.company}} and return their name.",
    "responseStructure": { "firstName": "string", "lastName": "string" }
  },
  "creditCost": 10,
  "next": { "stepId": "find-email" }
}

Knowledge & Data

Tool	Description
get_workspace	Get workspace info, company settings, active team members, pending invitations, and knowledge-list schemas
get_workspace_company_profile	Get the editable workspace company profile and company knowledge text
update_workspace_company_profile	Update top-level company profile fields like name, URLs, logo, industry, size, and additional information
update_workspace_executive_summary	Write the workspace-wide executive summary on the Workspace Assistant card
list_pinned_outputs	List output pages pinned to the workspace home/sidebar
set_output_page_pin	Pin or unpin a workflow output page on the workspace home/sidebar
list_knowledge_lists	List knowledge lists in the workspace
get_knowledge_rows	Get rows from a knowledge list (paginated via nextToken, max 200)
get_knowledge_rows_by_ids	Fetch specific rows by ID (max 200) — use after query_kg_edges
get_knowledge_text	Get text content from a knowledge entry
create_knowledge_list	Create a new knowledge list with a typed schema (idempotent on key collision)
update_knowledge_list_schema	Add or remove fields on an existing list schema
delete_knowledge_list	Permanently delete a list and all its rows
upsert_knowledge_rows	Insert or update rows in a list (max 500/call, per-row error reporting)
delete_knowledge_rows	Delete rows by ID
upsert_knowledge_text	Create or update a text knowledge entry
delete_knowledge_text	Delete a text knowledge entry by key
query_kg_edges	Query knowledge graph edges
get_scoring_history	Get scoring history for an entity

Credits and Cost Drivers

Credit reporting tools are opt-in for cost-driver detail so existing balance checks stay compact. Every credit total is ledger-derived and returned with a period object containing label, display, start, and end; always show that period label next to totals.

For a human/operator UI check, open the workspace credit usage page:

https://www.agentled.app/en/{workspace}/account/billing/credits-usage

Example: https://www.agentled.app/en/inovexus/account/billing/credits-usage.

Tool	Description
get_workspace_credits	Workspace balance, usage, executions, and recent ledger rows. Optional args: period, includeCostDrivers, includeRecentUsage, limit.
get_workspace_credit_cost_drivers	Convenience report with includeCostDrivers=true by default. Returns bounded top workflows, steps, models, and apps.
get_workflow_credits	Workflow-level usage and optional cost drivers, scoped to workflows in the authenticated API key workspace.

Supported periods:

• rolling-30-days: moving 30-day window ending at request time.
• rolling-7-days: moving 7-day window ending at request time.
• current-month / month-to-date: UTC calendar month-to-date, not the billing renewal period.
• previous-month: prior UTC calendar month.
• all-time: full ledger before the request time; use intentionally because it can scan more rows.

Examples:

// Compact balance/burn-rate check
{ "tool": "get_workspace_credits", "arguments": { "period": "rolling-30-days" } }

// Workspace cost drivers for a calendar month-to-date window
{ "tool": "get_workspace_credit_cost_drivers", "arguments": { "period": "current-month", "limit": 5 } }

// Workflow cost drivers
{ "tool": "get_workflow_credits", "arguments": { "workflowId": "wf_abc123", "period": "rolling-30-days", "includeCostDrivers": true } }

External API equivalents:

GET /api/external/workspace/credits?period=rolling-30-days&include=costDrivers&limit=5
GET /api/external/workflows/{workflowId}/credits?period=current-month&include=costDrivers

Billing-period reporting is separate from calendar-month reporting and should not be implied unless a future API adds an explicit billing-period label.

Branding (Whitelabel)

Tool	Description
get_branding	Get the workspace's whitelabel branding config (displayName, logo, colors, favicon, badge)
update_branding	Update branding — set displayName, logoUrl, tagline, primaryColor, primaryColorDark, faviconUrl, hideBadge

Agents

First-class workspace agents with identity, instructions, tools, config files, and assigned workflows. All agents are conversational (chat-only). For scheduled/autonomous work, attach routines via create_routine. SOUL.md and TOOLS.md live in configFiles; reflection context (JOURNAL.md, OBJECTIVES.md, PEOPLE.md) lives as linked AgentFiles and is auto-seeded for active chat-only reflection agents. Agents decide what durable signal belongs in those files; AgentLed only provides scoped storage and scheduled Reflection. An agent created entirely via MCP renders identically to one built in the Agent Wizard.

Tool	Description
list_agents	List agents in the workspace (filter by status: active, paused, draft)
get_agent	Get full agent config — instructions, files, workflows, attached routines
create_agent	Create an agent. Accepts agentType presets (personal-assistant, competitive-researcher, social-media-marketer, customer-support, content-marketer, lead-qualifier, deal-sourcer, custom), enabledApps, appPermissions, assignedWorkflowIds, linkedFileIds, configFiles (SOUL.md/TOOLS.md), avatar_icon_name, avatar_color, chatModel, activate: true
update_agent	Partial update — same fields as create_agent; updates.slug renames the agent email slug, moves the AgentEntity id to {slug}@{workspace}, and rebinds routines/file links/channel sessions/chat sessions where available
activate_agent	Activate an agent (draft/paused → active). Attached routines begin running on schedule
pause_agent	Pause an active agent. Attached routines stop until resumed
manage_agent_workflows	Add/remove/set the workflows assigned to an agent without rewriting the full config
delete_agent	Permanently delete an agent and all its files
chat_with_agent	Send a message to a specific agent. Multi-turn via session_id

Slug convention: slug is the short role ID used in URLs and email addresses. Keep Agent in the display name when useful, but do not append -agent to the slug just because the display name includes it; for example, Deal Sourcing Agent should use deal-sourcing@{workspace}.agentled.ai, not deal-sourcing-agent@{workspace}.agentled.ai.

Agent Files

Tool	Description
list_agent_files	List files attached to an agent (knowledge, context, reference docs)
get_agent_file	Get the content of a specific agent file
upload_agent_file	Upload a file (max 400KB text/markdown) to an agent
update_agent_file	Update a file already attached to an agent; use this after get_agent_file for JOURNAL.md, OBJECTIVES.md, and PEOPLE.md edits
delete_agent_file	Delete a file from an agent

Reflection files follow the same durable markdown pattern used by OpenClaw and Hermes memory surfaces: keep JOURNAL.md as a concise dated log, OBJECTIVES.md as active/completed goals, and PEOPLE.md as stable relationship context. Read first, update only when there is durable signal, and send full replacement content. See docs/AGENT_REFLECTION_FILES.md for the UI, MCP, and CLI editing contract.

Routines

Routines are scheduled prompts attached to an agent — the agent evaluates the prompt on a set interval and can trigger workflows or send notifications.

Example — add a daily deal-sourcer routine to an existing agent:

# Step 1: create the agent
create_agent({
  name: "Daily Deal Sourcer",
  agentType: "deal-sourcer",
  enabledApps: ["agentled", "kg", "web-scraping"],
  appPermissions: {
    kg: { access: "write", writeApprovalRequired: true },
    "web-scraping": { access: "read" }
  },
  assignedWorkflowIds: ["<opportunity-scoring-workflow-id>"],
  activate: true
})

# Step 2: attach a routine
create_routine({
  agent_id: "<agent-id>",
  name: "Daily Sourcing Run",
  prompt: "Find 5 new SaaS startups that match our deal criteria and trigger the scoring workflow for each.",
  interval: "daily"
})

Read access is implicit and never requires approval. The internal agentled app is selected as an app when needed but is not configurable in appPermissions.

Tool	Description
list_routines	List all routines for an agent
create_routine	Create a routine (name, prompt, interval)
update_routine	Update routine fields; recalculates nextRunAt if interval changes
pause_routine	Pause a routine
resume_routine	Resume a paused routine
trigger_routine	Run a routine immediately without changing its schedule
delete_routine	Permanently delete a routine

Interval values: weekday-morning, weekday-evening, weekly-monday, weekly-tuesday-evening, weekly-friday-evening, daily, monthly, 6h, 48h.

Deprecated Low-Level Runtime

Direct low-level monitor-runtime MCP tools are deprecated and no longer registered. Use create_agent / update_agent plus routines for autonomous work.

Channels (Email, Slack, WhatsApp, Signal)

Channels route inbound messages into the agent chat runtime. Each channel has a defaultAgentId that decides which agent handles the conversation. Replies are sent back through the originating channel.

Tool	Description
list_channels	List configured channels with their defaultAgentId, enabled state, and non-secret config (secrets redacted)
set_channel_default_agent	Assign the agent that handles a channel's inbound conversations
configure_channel	Update non-secret channel config — enabled, defaultAgentId, allowedSenders (email), defaultChannelId (slack)
set_channel_defaults	Update workspace-wide defaults: maxSessionsPerDay, sessionTimeoutMinutes, toolMode

Secret credentials (Slack bot tokens, signing secrets, WhatsApp access tokens, Signal webhook secrets) are NEVER readable or writable via the external API. Connect those via Settings → Channels in the UI — OAuth flows store them encrypted at rest.

Conversational Agent

Tool	Description
chat	Send a message to the AgentLed AI agent. Build workflows through natural language — no JSON required. Supports multi-turn conversations via session_id.

Chat Tool — Usage & Examples

The chat tool is a conversational AI agent that can reason, plan, and build workflows through dialogue. Think of it as the difference between gh api (raw) and gh copilot (intelligent).

Parameters:

Parameter	Type	Required	Description
message	string	Yes	The message to send to the AI agent
session_id	string	No	Session ID from a previous response, for multi-turn conversations

Response format:

{
  "response": "The agent's reply — may include workflow suggestions, explanations, or confirmation of actions taken",
  "sessionId": "mcp-chat-ws123-1711929600000"
}

Multi-turn conversation:

# Turn 1: Describe what you want
chat("Build me a workflow that enriches LinkedIn companies and scores them by ICP fit")
# → Agent responds with a plan and creates a draft workflow
# → Response includes sessionId: "mcp-chat-ws123-1711..."

# Turn 2: Iterate on the design
chat("Add an email finding step using the company domain", session_id: "mcp-chat-ws123-1711...")
# → Agent modifies the workflow, maintaining conversation context

# Turn 3: Finalize
chat("Looks good, publish it as live", session_id: "mcp-chat-ws123-1711...")
# → Agent publishes the workflow

When to use chat vs structured tools:

Use chat when...	Use structured tools when...
You have a high-level goal	You know the exact pipeline JSON
You want AI recommendations	You need precise control
You're exploring available skills or integrations	You're automating a known pattern
You want to iterate through dialogue	You're doing bulk operations

Notes:

• 5-minute timeout — break complex requests into smaller steps if needed
• Responses are non-streaming (collected then returned) for MCP client compatibility
• The agent has full access to planning tools, workflow builder, and workspace context
• Error responses include actionable messages (e.g., timeout → "try breaking it into smaller steps")

Intent Router

Tool	Description
do	Natural language intent router — describe what you want and it auto-selects and executes the right tool

Help & Feedback — Talk to the Agentled Team

For AI agents — pick the right channel:

What you hit	Where it goes
Workflow not behaving as expected, app/integration question, platform/billing question, missing feature, anything about how Agentled works	Call submit_feedback_to_agentled (below). Routes straight to the team.
Bug in the CLI itself (agentled command crashes, writes wrong data to .agentled/, flag doesn't work) — a reproducible CLI code defect	File a GitHub issue at https://github.com/agentled/mcp-server/issues. Include CLI version + reproduction steps.
Anything else, or you're not sure	submit_feedback_to_agentled — the team will reroute if needed.

Do not search npm metadata, scrape GitHub, or shell out to gh to "find" a bug tracker — the routes above are the only ones. The bugs field in the package points at the human contact page (https://www.agentled.ai/en/contact-us) on purpose.

For humans: email contact@agentled.ai or visit https://www.agentled.ai/en/contact-us.

Tool	Description
submit_feedback_to_agentled	Ask a question, file a bug, request a feature, or escalate an issue. Types: ask, bug, feature_request, escalation. Provide userEmail if you want a reply.

Coming from n8n?

Import existing n8n workflows and make them AI-native:

Tool	Description
preview_n8n_import	Preview an n8n workflow import (dry run)
import_n8n_workflow	Import an n8n workflow into Agentled

Looking Up Entity-Scoped Data

When you need all records related to a specific entity, use the two-tool chain instead of paginating get_knowledge_rows:

Example 1 — all deals scored by an investor:

1.  query_kg_edges({ entityName: "Investor Name", relationshipType: "SCORED" })
    → returns edges with targetNodeIds

2.  get_knowledge_rows_by_ids({ rowIds: <targetNodeIds from step 1> })
    → returns full row data for each matched deal

Example 2 — all leads sourced from a campaign:

1.  query_kg_edges({ entityName: "Campaign Name", relationshipType: "SOURCED" })
    → returns edges with targetNodeIds

2.  get_knowledge_rows_by_ids({ rowIds: <targetNodeIds from step 1> })
    → returns full contact/lead rows

Why this matters: get_knowledge_rows is limited to 200 rows per call. At 3k rows that means 15 round trips; at 10k it means 50. The KG-edge path is O(edges for that entity) — independent of total list size — so it stays fast regardless of how large the list grows.

Node ID convention: source_node_id and target_node_id values from query_kg_edges are knowledge row IDs. Rows outside the authenticated workspace are silently excluded.

For Agencies: White-Label Ready

Build workflows once, deploy to multiple clients under your own brand. Configure branding directly from the MCP server:

"Set my workspace branding: displayName 'Acme AI', primaryColor '#6366f1', tagline 'Powered by Acme'"

Use get_branding and update_branding to manage displayName, logo, colors, favicon, tagline, and badge visibility. Client portal appearance updates instantly.

Persistent Memory — Examples

Memories let workflows learn across executions. Store what worked, recall it next time.

Store a fact after enrichment

"Store a memory: key 'icp_criteria', value { industry: 'fintech', minEmployees: 50, region: 'EU' },
category 'preference', scope 'workspace'"

Recall before scoring

"Recall memory 'icp_criteria' at workspace scope — use it to score this batch of leads"

Search for past outcomes

"Search memories for 'conversion rate' in the 'outcome' category"

Track a running metric

"Store memory: key 'total_leads_processed', value 43, merge 'increment', scope 'workspace'"

Each subsequent call with merge: 'increment' adds to the existing value — no read-modify-write needed.

Outreach PCPL

For email or outbound workflows, track PCPL as a business metric:

PCPL = prospects contacted / positive replies

Use analyticsConfig for contacted prospects, positive replies, and PCPL. Literal PCPL should use a ratio metric with ratioMode: "raw"; positive reply rate should use the default percentage ratio.

Routines — Examples

Routines are scheduled prompts attached to agents. Use them for autonomous work such as daily checks, weekly digests, and workflow follow-up.

Create an agent with a daily sourcing routine

"Create a deal sourcing agent, then add a daily routine that checks the
incoming-leads knowledge list and starts the lead-enrichment workflow for
qualified new rows. Limit the routine to 10 workflow starts per day."

Tool sequence:

create_agent({
  name: "Daily Deal Sourcer",
  agentType: "deal-sourcer",
  enabledApps: ["agentled", "kg"],
  assignedWorkflowIds: ["wf_abc123"],
  activate: true
})

create_routine({
  agent_id: "<agent-id-or-slug>",
  name: "Daily New Lead Review",
  prompt: "Review incoming-leads, identify qualified new rows, and start the lead-enrichment workflow for each. Do not start more than 10 workflow runs in one day.",
  interval: "daily",
  max_steps_per_run: 20,
  max_credits_per_day: 50
})

Create a weekly workflow health routine

"Add a weekly routine to the operations agent that reviews workflow execution
history, flags abnormal failures, and notifies me only when action is needed."

create_routine({
  agent_id: "operations",
  name: "Weekly Workflow Health Review",
  prompt: "Review recent workflow execution history. If failures or stalls require action, summarize the affected workflows, likely impact, and recommended next step. Otherwise record that no action is needed.",
  interval: "weekly-monday"
})

Pause and resume

"Pause routine <routine-id>"
"Resume routine <routine-id>"
"Run routine <routine-id> now"

Works With

• Claude Code (Anthropic)
• Codex (OpenAI)
• Cursor
• Windsurf
• Any MCP-compatible client

Links

• Agentled Platform
• npm Package
• GitHub
• Report Issues

Building from Source

git clone https://github.com/Agentled/mcp-server.git
cd mcp-server
npm install
npm run build

License

MIT