Verificate MCP
Merge gate for AI-written code: deterministic vetoes plus a Claude Haiku 5.5 review. No signup.
| Endpoint | https://mcp.verificate.ai/mcp (streamable-http) |
|---|---|
| Registry version | 1.9.0 |
| Now | 5 tools · grade D (58) · pin ts_e6578923ca5f9463dea96668323d2fed · protocol 2024-11-05 |
| First / last listed | 2026-11-10 06:07 UTC / 2026-11-10 06:07 UTC |
| Grade over time | 2026-11-10: D (58), 5 tools |
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Tools: validate_artifact validate_ai_output validate_plan analyze_code generate_code
validate_artifact | Deterministic production-readiness gate for AI-built systems. Verifies the invariants that stop a system silently shipping broken: every critical component is PRESENT and LOADS, the import closure resolves (nothing assumed 'already on the box'), all runtime dependencies are declared, and health is a REAL fail-closed check. Returns approve/reject with a fix plan and ISO 27001 / ISO 5055 control evi params: artifact* |
validate_ai_output | The merge gate for ANY AI-written output — code, documentation, reports, emails, configs: returns a binary approve/reject verdict with veto power — e.g. it rejects code calling the nonexistent stripe.Inventory API, an N+1 loop with the latency arithmetic to prove it, or a doc claiming success with no evidence. Deterministic checks (mock/placeholder veto, reward-gamed tests, standard-library calls params: ai_output*, validation_type, context, expected_input_sha256, previous_receipt_id |
validate_plan | The gate for PLANS, designs and specs — run BEFORE any code is written, the cheapest place to catch a bad design. Returns the same binary verdict shape as validate_ai_output, with findings on completeness, feasibility, performance and scalability implications, security risks and missing considerations (e.g. it rejects a plan that polls an API every 100ms per client, with the request-volume math). params: plan*, context, expected_input_sha256, previous_receipt_id |
analyze_code | Advisory deep-dive on existing code — scores and findings, deliberately NO pass/fail verdict, so it never blocks an agent. Surfaces performance hot paths, scalability cliffs, reliability gaps and tech debt with concrete latency/throughput arithmetic (e.g. 'O(n²) dedup: ~4s at 10k items'). Read-only: the code is analyzed, never executed. Use it to understand a validate_ai_output rejection or review params: code*, analysis_type, language |
generate_code | Generate code and gate it in one step: an LLM writes the implementation, then the same protection engine as validate_ai_output vets it — retrying generation when the gate rejects. If every attempt is vetoed you still receive the last attempt, clearly marked validated:false with the gate findings — rejected code is never presented as clean. Generation runs on our infrastructure; nothing executes in params: prompt*, language, max_tokens |
Entries describe changes in tool definitions. They are not statements about a server, its quality or its author. The grade measures how the tool definitions fit model providers (token cost, schema compatibility, confusable names) as scored by the ToolFit engine at the time of the listing. JSON: append .json. Opt-out: how or hello@youraitools.co.