mcp server
Verificate MCP
Agentic code review, no signup to try: reality gates + frontier-model review, with veto.
Description as published by the maintainer. Source
- version 1.8.7
- active
- code review
active — Most recent push to the repository was 2026-08-05. Dashed tags are derived by ZBS Index from the published description, not stated by the maintainer.
What this server can do
4 functions, named and described by the server itself. Parameter names are shown because they say more about what a function does than its name usually does.
analyze_code(code, language, analysis_type)- 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 inherited code; use validate_ai_output when you need an accept/reject decision. Required: code.
generate_code(prompt, language, max_tokens)- 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 your environment. Required: prompt.
validate_ai_output(context, ai_output, validation_type)- 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 reality gates (mock/placeholder veto, gaming and bypass detection, invented-API checks) run first and cannot be overridden; a frontier-model review (ISO/IEC 25010) then scores quality, accuracy, reliability and tech debt. In a benchmark, a frontier model reviewing alone caught reward-gaming and hallucinated APIs 0/6 times in a natural review workflow; these gates catch them deterministically on every call. Read-only: nothing is executed. Call it on every AI-generated deliverable before accepting it; use validate_plan for plans, analyze_code for an advisory report without a verdict. Required: ai_output.
validate_plan(plan, context)- 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). Read-only: nothing is executed or stored beyond the verdict. Use validate_ai_output for the code that follows. Required: plan.
Last successful function declaration observed on . Source: https://mcp.verificate.ai/mcp. We list what the server declared; we do not call any of these functions.
Endpoint status observed on . Source: https://mcp.verificate.ai/mcp.
Signals
These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.
| Signal | Value | What it measures | Window | Observed | Source |
|---|---|---|---|---|---|
| GitHub stars | 0 | Number of GitHub accounts that bookmarked this repository since it was created. It is a bookmark count, not installs, not active users and not quality. | cumulative, all time | GitHub | |
| Last commit | 2026-08-05 | Date of the most recent push to any branch. This is the strongest cheap indicator of whether the project is still maintained. | point in time | GitHub | |
| Open issues | 0 | Open issues plus open pull requests, as GitHub counts them together. A high number can mean an active project or an abandoned one. | as of fetch | GitHub | |
| Latest published version | 1.8.7 | Latest version string the maintainer published to the registry. | as of fetch | Model Context Protocol | |
| Registry record last updated | 2026-08-01 | When the registry record was last updated by its maintainer. | point in time | Model Context Protocol | |
| License | MIT | Licence GitHub detected in the repository. Detection can be wrong; the LICENSE file is authoritative. | as of fetch | GitHub | |
| First listed in the MCP Registry | 2026-08-01 | Date this server was first published to the official MCP Registry. Not a usage or quality measure. | point in time | Model Context Protocol | |
| repository status | active | The repository exists on GitHub and is not archived. This says nothing about how recently it was worked on. | as of fetch | GitHub | |
| mcp tools declared | 4 tools | Number of functions the server itself declared when asked to list them. This is what the server offers an agent, not a measure of how well any of them work. | as of probe | mcp.verificate.ai | |
| mcp endpoint status | ok | The server listed 4 functions when asked. | as of probe | mcp.verificate.ai |
Where to get it
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These share tags the maintainers applied themselves, such as llm-validation, ai-code-review, code-quality. Common tags like "mcp" or "ai" are ignored for this: agreeing with six hundred other projects is not a similarity.
This is not a recommendation and not a test result. It is a map of what the authors said their work is about.
How the author describes it
Topics the maintainer set on GitHub: ai-code-review, claude, code-quality, cursor, developer-tools, llm-validation, mcp, mcp-server.
This record as data
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