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.
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 paramsAfter 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...
outputstringschemaobjectlanguagestringoverridebooleandirectivesarraycheck_typesarrayoverride_reasonstringguardrail_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 paramsEvaluate 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.
agent_idstringproposed_actionobjectguardrail_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 paramsCreate 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...
namestringrulesarrayprioritynumberdescriptionstringaction_typesarrayio.github.ericm1018/skillfm-llm-cost-optimizer-openai-anthropic-usage
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io.github.mikerawsonnz/authenticated-multi-llm-agent