CAT
/MCP
SkillsMCPMarketplacesDigestToolsAdvertise

This week in Claude

Every Monday: Claude Code, Agent SDK, MCP, and the Anthropic platform moves worth your time.

Skills by Category
Frontend DevelopmentBackend & APIsTesting & QASecurityDevOps & CI/CDGit & Pull RequestsDocumentationCode Review & QualityAI & Agent BuildingSkill Development
MCP Servers by Category
Sales & MarketingWeb & Browser AutomationDatabasesAI & LLM ToolsCloud & InfrastructureCommunication & MessagingDeveloper ToolsDesign & CreativeDocuments & KnowledgeSearch & Web Crawling
Marketplaces by Category
AI Agents & OrchestrationLLM IntegrationDevelopment ToolsFrontend & UIBackend & APIsDatabasesTesting & Code QualityDevOps & CloudSecurity & ComplianceGit & Version Control

Cross AI Tools

Discover Claude Code plugins, extensions, and tools. Automatically updated directory of Anthropic Claude AI marketplaces with development tools, productivity plugins, and integrations.

Resources

  • Browse Skills
  • Browse MCP Servers
  • Browse Marketplaces
  • Plugins Reference

Community

  • About
  • Tools
  • Feedback
  • Privacy Policy
  • Advertise

Built for the Claude Code community with Claude Code by @mertduzgun

Independent project, not affiliated with Anthropic

Contextful

inferensys/contextful
1STDIOregistry active
Summary

This is an alpha-stage context engine that indexes your codebase once and returns ranked, cited evidence packs instead of making agents repeatedly read dozens of files. It exposes seven MCP tools, anchored by context_pack(query, budget, scope), which replaces scattered grep and file read calls with a single token-budgeted response. You also get search_code forlex/BM25 search, trace_path for graph traversal across symbols and modules, impact_analysis for reverse dependencies, and write_lesson for storing evidence-backed memory that persists across sessions. Uses SQLite with FTS5 for local-only indexing. Set it up with npx @inferensys/contextful init, then run it as an MCP server. Reach for this when context window bloat and redundant file reads are slowing down your agent workflows.

CodeRabbit
CodeRabbit
AI writes the code. CodeRabbit catches the slop.
Try For Free →
Keep your Mac awake
Keep your Mac awake
Keep your Mac awake while Claude Code and 40+ AI agents run. Sleeps when they're idle.
One time payment $9 →
Context.devContext.dev
Context.dev
Integrate web data into your AI product. One API to scrape website & brand data.
Get API Key Now →
Make your agent a DeFi expert
Make your agent a DeFi expert
Agent, run crypto. Access onchain data & trade routes via 1inch.
Install now →
Make money from your Skills
Make money from your Skills
On Capafy, your Skill runs online 24/7 as an agent product, and you get paid every time someone uses it.
Start earning →
AppSignal
AppSignal
Monitor with ease. Code with confidence.
Start Free Trial →
CodeRabbit
CodeRabbit
AI writes the code. CodeRabbit catches the slop.
Try For Free →
Keep your Mac awake
Keep your Mac awake
Keep your Mac awake while Claude Code and 40+ AI agents run. Sleeps when they're idle.
One time payment $9 →
Context.devContext.dev
Context.dev
Integrate web data into your AI product. One API to scrape website & brand data.
Get API Key Now →
Make your agent a DeFi expert
Make your agent a DeFi expert
Agent, run crypto. Access onchain data & trade routes via 1inch.
Install now →
Make money from your Skills
Make money from your Skills
On Capafy, your Skill runs online 24/7 as an agent product, and you get paid every time someone uses it.
Start earning →
AppSignal
AppSignal
Monitor with ease. Code with confidence.
Start Free Trial →

contextful cover image

Contextful

⚠️ This is in early alpha. The API, features, and integrations are not stable. Expect breaking changes. Feedback & contributions are very welcome!

Local Context Management + Search Engine + Memory for Agentic AI.

Contextful is a runtime contextual layer and local search engine for agents that gives them one fast way to find, compress, cite, and remember project context.

Available as a CLI-first tool with an MCP runtime bridge and generated agent instructions, it integrates seamlessly with Codex, Claude Code, Cursor, Windsurf, GitHub Copilot, VS Code, Cline, Roo Code, Continue, and Zed.

Contextful screenshot

Instead of making an agent read 40 files every session, Contextful indexes the project once and returns a ranked, cited, token-budgeted context pack.

Why?

Context has always been a bottleneck for agentic AI. Large context window models (for example, 1M tokens) are:

  1. Expensive and require significantly more compute & processing time.
  2. More likely to lose key information as the context window fills up.
  3. Most projects have millions of lines of code, but agents can only fit in limited tokens per context window.

The current solution is to make the agent guess which files to read, then pay the token cost to read them every session. This is slow, expensive, and lossy.

Apart from this, agents have no way to store or share learnings across sessions. Every time they start, they forget everything and have to re-read the same context again.

Contextful screenshot

I started developing Contextful to keep the context window smaller by enabling efficient knowledge retrieval. If we index the project and return a ranked, cited, token-budgeted context pack, we can:

  • 100x more efficient token usage: stop paying tokens to re-read the same files.
  • Fewer tool calls: one context pack can replace dozens of grep, glob, and read-file calls.
  • No lost context between sessions: agents can store session learnings in an evidence-backed memory ledger.
  • Shareable project knowledge: lessons and context packs survive context compaction and future sessions.

Key Features

1. Context Management

The default local store is SQLite with FTS-backed search and typed graph tables. V1 ships with:

  • SQLite as the default local store.
  • FTS5 lexical/BM25 search.
  • Typed graph tables: nodes, edges, node_props, edge_props.
  • A hot adjacency cache for common graph relations.
  • Deterministic structural fingerprints inspired by Code2Vec-style secondary reranking signals.

The next storage upgrades are optional semantic vectors through sqlite-vec, LanceDB, or local HNSW, and compressed adjacency lists with Roaring bitmaps or CSR arrays for larger repositories.

2. Search Engine

Contextful screenshot

Contextful analyzes the query, classifies intent, and combines lexical search, symbols, docs, graph relationships, and memory hits to retrieve the right evidence. The goal is Google-level project search for agents: vague queries like "resources for auth onboarding" should still land on the right code, docs, and prior lessons.

3. Memory Ledger

Agents can store lessons, decisions, and useful project facts, but not as loose "remember this" notes. Every memory requires evidence refs from files, symbols, commits, or prior context packs. When the evidence changes, Contextful marks the memory stale.

4. Contextful Execution

Contextful is an MCP server, local indexer, and small CLI:

  • MCP server: the agent interface.
  • Local daemon / watcher: indexing, rebuilds, freshness, and future benchmarks.
  • CLI (cxf): human setup, indexing, search, memory writes, and local smoke tests.

MCP is the right interface because tools, resources, and prompts are exactly what MCP standardizes. The agent asks for context; Contextful returns compact evidence.

Install

npx @inferensys/contextful init --workspace .
npx @inferensys/contextful search "where is user auth handled" --workspace . --budget 2000

Run as an MCP server:

npx @inferensys/contextful server

CLI

The primary binary is cxf; contextful is also provided as a readable alias.

cxf init --workspace <path>
cxf index --workspace <path> [--watch]
cxf daemon --workspace <path>
cxf search "<query>" --workspace <path> --budget 2000 --json
cxf memory add --workspace <path> --claim <text> --evidence <ref>
cxf server

Core MCP Tools

Keep the agent surface small:

  • context_pack(query, budget, scope) - the killer tool. Returns a ranked, cited, token-budgeted bundle instead of forcing 40 random file reads.
  • search_code(query, mode, filters) - powerful code, docs, symbol, and memory search.
  • trace_path(from, to, edge_types) - graph traversal across files, symbols, modules, and config.
  • impact_analysis(symbol_or_file) - reverse dependencies and likely tests.
  • why_changed(symbol_or_file) - current evidence plus git history.
  • recall_memory(query, scope) - search session learnings and durable project lessons.
  • write_lesson(claim, evidence_refs, scope) - store an evidence-backed memory.

MCP Client Setup

Use this stdio server command in any MCP-aware coding tool:

{
  "mcpServers": {
    "contextful": {
      "command": "npx",
      "args": ["-y", "@inferensys/contextful", "server"]
    }
  }
}

Codex:

codex mcp add contextful -- npx -y @inferensys/contextful server

CLI-First Agent Flow

Use cxf init once per workspace. It indexes the project and writes .contextful/AGENT_INSTRUCTIONS.md, a compact skill-style guide that tells agents when to call context_pack, when to search more narrowly, and when memory writes are allowed.

Use cxf search when a human wants to test the same evidence pack an agent will receive:

cxf search "how does auth load user profiles?" --workspace . --budget 2000

The MCP server remains the agent interface. The CLI is for setup, inspection, and repeatable local tests.

Privacy

V1 is local-only. It does not call external embedding APIs, upload source code, edit source files, auto-fix code, or install dependencies inside the target workspace.

Evidence Refs

Memory writes require evidence references returned by search or context packs:

  • file:src/auth.ts:10-40
  • symbol:src/auth.ts#AuthService:12
  • pack:ctx_...

Invalid or stale evidence is rejected or marked stale.

Featured
CodeRabbit
CodeRabbit
AI writes the code. CodeRabbit catches the slop.
Try For Free →
Keep your Mac awake
Keep your Mac awake
Keep your Mac awake while Claude Code and 40+ AI agents run. Sleeps when they're idle.
One time payment $9 →
Context.devContext.dev
Context.dev
Integrate web data into your AI product. One API to scrape website & brand data.
Get API Key Now →
Make your agent a DeFi expert
Make your agent a DeFi expert
Agent, run crypto. Access onchain data & trade routes via 1inch.
Install now →
Make money from your Skills
Make money from your Skills
On Capafy, your Skill runs online 24/7 as an agent product, and you get paid every time someone uses it.
Start earning →
AppSignal
AppSignal
Monitor with ease. Code with confidence.
Start Free Trial →
Categories
AI & LLM ToolsSearch & Web Crawling
Registryactive
Package@inferensys/contextful
TransportSTDIO
UpdatedMay 15, 2026
View on GitHub

Related AI & LLM Tools MCP Servers

View all →
SkillFM LLM Cost Optimizer

io.github.ericm1018/skillfm-llm-cost-optimizer-openai-anthropic-usage

LLM cost optimizer for OpenAI, Anthropic, token usage, BYOK, and SkillFM Beacon audits.
Llm Orchestration Agent

io.github.mikerawsonnz/llm-orchestration-agent

Run a prompt through a LangChain (system + human) chain over Gemini on Vertex AI; optional LangSmith
Authenticated Llm Agent

io.github.mikerawsonnz/authenticated-llm-agent

JWT-gated LLM gateway: authenticate (bcrypt/JWT), then run a LangChain-on-Vertex Gemini completion.
Copilot Memory MCP

labforgedev/copilot-memory-mcp

Persistent semantic memory for AI agents using local ChromaDB vector search. No cloud required.
1
Agent Prompt Injection Firewall Mcp

csoai-org/agent-prompt-injection-firewall-mcp

The WAF for agents. Pattern-based + heuristic firewall scans prompts, RAG documents, tool argume...
Authenticated Multi Llm Agent

io.github.mikerawsonnz/authenticated-multi-llm-agent

Google-OAuth-gated LLM gateway: verify a Google ID token, then run a Gemini (Vertex AI) completion f