Claude MarketplacesSubmit
qualequest avatar

QualeQuest — AI Agent Governance Platform

com.qualequest/governance

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.

tools
3
transport
HTTP
last updated
Apr 5, 2026
Open website

Tools

3 tools

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

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
check_typesarray
Checks to run. Auto-inferred if omitted.
directivesarray
Directive objects. Types: must_include, must_not_include, must_match, must_not_match, must_contain, must_not_contain, min_length, max_length.
languagestring
Code language for syntax check: json, python, javascript, typescript.
outputstring
The agent output text to validate.
overrideboolean
Force pass. Requires override_reason.
override_reasonstring
Required when override is true.
schemaobject
JSON Schema to validate output against.
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
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 type...
descriptionstring
Optional human-readable summary of what this policy enforces. Returned in guardrail_check responses and guardrail_list_policies output for auditability.
namestring
Unique policy name per org. Examples: 'no-delete-in-prod', 'budget-cap-50', 'pii-block'.
prioritynumber
Optional. Evaluation order. Default: 0.
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 su...

Sponsors

Promote your product hereReach 125k+ visitors monthlyPromote your product hereReach 125k+ visitors monthlyAgent CaffeineSponsoredClose your lid, come back to finished workSnugSponsoredThe clipboard manager for the AI eraPromote your product hereReach 125k+ visitors monthlyWaxSponsoredBlock anything with supervision, without erasing your iPhonePromote your product hereReach 125k+ visitors monthlyknobsSponsoredSee your site on any phone, any day, in any language

Details

Website
gaas.qualequest.com
Categories
AI & LLM Tools, Productivity & Office
Transport
HTTP
Registry
MCP registry, active

Related AI & LLM Tools MCP servers

View all
1dobo123123 avatarDoboCachedobo123123/dobocacheMCP server for LLM prompt-cache observability: prefix diagnostics and per-call white-box report.0stars
2fellanh avatarContext Vaultfellanh/context-vaultHosted memory for AI agents: save what your agent learns and recall it in the next session.0stars
3fusionpacttech avatarFusionpact Vectordbfusionpacttech/fusionpact-vectordbHybrid vector + reasoning retrieval, agent memory, multi-agent orchestration, MCP server, and RAG.0stars
4hadinkof avatarCapabilitymap Nexus Mcphadinkof/capabilitymap-nexus-mcpGitHub Capability Router (666 repos, 96.4% token savings) & 5-Vector Prompt-Injection Firewall.0stars
5mrdulasolutions avatarDocDeploymrdulasolutions/docdeployDocument hosting and encrypted agent memory with multi-tenant persistence.0stars
6akhilucky avatarAI Firewall MCPakhilucky/ai-firewallMulti-agent LLM security layer detecting prompt injection and jailbreaks.0stars