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QualeQuest — AI Agent Governance Platform

com.qualequest/governance
3 toolsHTTPregistry active
Summary

This server connects Claude to QualeQuest's adversarial governance system, which puts operational plans through a seven-phase trial process: filing, charges, evidence, prosecution, defense, verdict, and sentencing. You'd reach for this when you need a decision validated before committing resources, especially for AI deployments, procurement, or scaling decisions. It extracts load-bearing claims from your plan, grades supporting evidence, builds the failure case, and issues binding verdicts with tripwires. The workflow takes your decision title, plan summary, failure definition, and risk tolerance, then runs an automated trial that returns a complete packet. Think structured red-teaming as a service rather than consulting calls.

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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.
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Make money from your Skills
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On Capafy, your Skill runs online 24/7 as an agent product, and you get paid every time someone uses it.
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Tools

Public tool metadata for what this MCP can expose to an agent.

3 tools
qualitygate_validateAfter your agent generates output, validate it against your rules before shipping. Runs deterministic checks (regex, JSON schema, syntax) plus optional LLM-powered tone and factual analysis. Returns a structured verdict (pass, warn, or fail) with a 0-100 score and per-check is...7 params

After your agent generates output, validate it against your rules before shipping. Runs deterministic checks (regex, JSON schema, syntax) plus optional LLM-powered tone and factual analysis. Returns a structured verdict (pass, warn, or fail) with a 0-100 score and per-check is...

Parameters* required
outputstring
The agent output text to validate.
schemaobject
JSON Schema to validate output against.
languagestring
Code language for syntax check: json, python, javascript, typescript.
overrideboolean
Force pass. Requires override_reason.
directivesarray
Directive objects. Types: must_include, must_not_include, must_match, must_not_match, must_contain, must_not_contain, min_length, max_length.
check_typesarray
Checks to run. Auto-inferred if omitted.
override_reasonstring
Required when override is true.
guardrail_checkEvaluate a proposed agent action against your governance policies. Returns allow or deny with the matched policy reason. Requires at least one active policy created via guardrail_create_policy. Deterministic rule evaluation — no LLM. Costs 1 credit.2 params

Evaluate a proposed agent action against your governance policies. Returns allow or deny with the matched policy reason. Requires at least one active policy created via guardrail_create_policy. Deterministic rule evaluation — no LLM. Costs 1 credit.

Parameters* required
agent_idstring
Agent identifier.
proposed_actionobject
Action to evaluate. Must contain a 'type' field. Example: {"type": "http_request", "url": "https://external.example.com"} or {"type": "file_write", "path": "/etc/config"}.
guardrail_create_policyCreate a persistent governance policy that guardrail_check evaluates on every subsequent call. Define rules using and/or/not operators over action types, resource patterns, and budget thresholds. Call this before using guardrail_check — checks require at least one active polic...5 params

Create a persistent governance policy that guardrail_check evaluates on every subsequent call. Define rules using and/or/not operators over action types, resource patterns, and budget thresholds. Call this before using guardrail_check — checks require at least one active polic...

Parameters* required
namestring
Unique policy name per org. Examples: 'no-delete-in-prod', 'budget-cap-50', 'pii-block'.
rulesarray
Array of rule objects evaluated against the proposed_action in guardrail_check. Leaf operators: eq, starts_with, contains, gt, lt (compare field to value). Compound operators: and, or, not (nest sub-rules in a rules array). Example: [{operator:'eq', field:'type', value:'file_write'}] blocks all file writes. Nested example: [{operator:'and', rules:[{operator:'eq',field:'type',value:'api_call'},{operator:'contains',field:'url',value:'prod'}]}] blocks prod API calls.
prioritynumber
Optional. Evaluation order. Default: 0.
descriptionstring
Optional human-readable summary of what this policy enforces. Returned in guardrail_check responses and guardrail_list_policies output for auditability.
action_typesarray
Optional. Restrict this policy to only evaluate when proposed_action.type matches one of these values. Examples: ['file_write', 'api_call', 'db_delete']. Omit to apply the policy to all action types regardless of type field.
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Keep your Mac awake while Claude Code and 40+ AI agents run. Sleeps when they're idle.
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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
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Agent, run crypto. Access onchain data & trade routes via 1inch.
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Categories
AI & LLM ToolsProductivity & Office
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TransportHTTP
UpdatedApr 5, 2026
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