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MCPVetter

NPM · MCPVETTER · 2 COMPONENTS

Audit your MCP servers for poisoned tools and leaked secrets, and check AI-generated code for flaws.

66 Trust /100

Last scanned (4 hours ago)

Scores higher than 33% of npm servers

Category Security

Trust breakdown (7 categories)

How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score → Why this is hard to score →

Supply Chain Security81
  • No malware found by supply-chain analysis.Pass
  • CVE check failed: a known high-severity CVE affects zod 3.25.76, a direct dependency. A fixed version is available. View diagnostics → Fail
  • No install/post-install scripts declared.Pass
  • 30 of 93 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency48
  • Source repository is publicly reachable at the declared URL. View diagnostics → Pass
  • Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
  • Clear OSI-approved license (MIT).Pass
  • Actively maintained (last published 2 days ago).Pass
  • Publishes a security disclosure policy (SECURITY.md).Pass
Schema Quality & AI Usability78
  • AI-judged instruction clarity (excellent).Pass
  • Context-footprint check failed: tool/resource definitions use about 923 tokens (~131/item across 7 items; 7 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
  • Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management0
  • Stability not yet verified: not enough scan history yet (needs a 30-day window).Unverified
Tool Coverage100
  • 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
  • 100% of tool parameters carry a description.Pass
Tool Safety100
  • No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
  • We read all 7 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
  • An AI judge read all 8 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
  • Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass

Unverified: 1 category

A category scored 0 because we could not verify it: a data source with nothing on this package, evidence we could not reach, or a check we could not run. We only credit what we can confirm.

Changelog

Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.

  • 4 Nov 26 +5
    • CVE-2023-54404 affects this package: high ▼ security
    • Known CVEs: unverified → fail ▼ security
    • Dependency health: unverified → 0.85 ▲ functional
  • 3 Nov 26 61

    First indexed and scored.

Diagnostics

Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.

Captured 5 Nov 2026 · Analysed npm/mcpvetter@0.1.3

Provenance No attestation

The registry publishes no build provenance for this version, so there is nothing to verify.

Result No attestation
Ecosystem npm

Background: How many MCP packages publish verified provenance →

Vulnerabilities 1 finding
ID CVE Severity Vector Fix available
GHSA-mrhx-c7xc-m3w5 CVE-2023-54404 high CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H yes

Background: What a vulnerability scan can and cannot prove →

Dependencies 93 packages
Packages resolved 93
Stale 30
Tree resolution Complete

Background: SBOMs and build attestations, explained →

MCP tools · 7 exposed · ~831 tokens

The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →

Tool Tokens
check_lab ~76

Verify a fixed snippet for a lab started with start_lab. Runs the lab's security checks on the code and records progress. Call this when the user says their lab fix is ready.

NameTypeReqDescription
codestringyesThe complete fixed code.
lab_idstringyesThe lab id, e.g. lab-02-sqli.

No output schema declared.

No examples provided.

check_mcp_setup ~126

Audit the MCP servers configured on this machine (Claude Code, Claude Desktop, Cursor, VS Code, Windsurf, Gemini CLI) for leaked secrets, unpinned or look-alike packages, risky launch commands, unencrypted remote servers, over-broad file access, and dangerous server combinations. Call this when the user asks whether their MCP setup or a server is safe. Static and local; for tool-description poisoning run `npx mcpvetter scan --live` in a terminal.

NameTypeReqDescription
configstring–Optional pasted config JSON; omit to scan this machine.

No output schema declared.

No examples provided.

explain_finding ~106

Explain an MCPVetter finding in plain English: why it matters, how the flaw is triggered (in general, defensive terms), why a code reviewer would block it, and how to fix it. Call this when the user asks what a finding ID (like CODE-SQLI-001 or MCP-POISON-001) means or why it matters.

NameTypeReqDescription
idstringyesFinding ID, e.g. CODE-SQLI-001 or MCP-SECRET-001.

No output schema declared.

No examples provided.

review_code ~172

Check a code snippet for security flaws before presenting or committing it. Call this before showing the user any generated code that handles authentication, sessions, database queries, user or LLM input, HTML rendering, file uploads, outbound URL fetches, secrets, or MCP configuration. Returns prioritised findings, each with evidence, a concrete fix, and a 5-minute lab. Analysis is static and deterministic; it checks at the moment of generation and is not a guarantee.

NameTypeReqDescription
ai_generatedboolean–True if an AI assistant produced this code.
codestringyesThe code to review (a snippet or a whole file).
filenamestring–Optional file name, used for language detection only.
languagestring–javascript, typescript, python, or json. Detected if omitted.

No output schema declared.

No examples provided.

secure_pattern ~156

Return a vetted secure code example for a pattern in the user's stack, the insecure version to avoid, and a reviewer checklist. Call this when writing code for: database queries, secrets/API keys, login and sessions, rendering user content as HTML, sending user input to an LLM, using LLM output, file uploads, fetching URLs/webhooks, or configuring MCP servers. Example requests: "parameterised query in Node", "safe handling of LLM output in Python".

NameTypeReqDescription
requeststringyesWhat you need, e.g. 'parameterized query in Node' or a pattern id like 'url-fetching'.
stackstring–Optional stack hint: node, python, express, django, fastapi, next...

No output schema declared.

No examples provided.

start_lab ~127

Start a short hands-on lab that fixes one vulnerability in realistic AI-generated code. Call this when the user wants to learn how to fix a finding, or after review_code reports a finding with a lab. Pass a lab id (lab-02-sqli) or a finding id (CODE-SQLI-001). Then help the user fix the snippet and submit it with check_lab.

NameTypeReqDescription
finding_idstring–Finding id; the matching lab is opened.
lab_idstring–Lab id, e.g. lab-02-sqli. Omit to list labs.

No output schema declared.

No examples provided.

team_posture ~68

Team plan: aggregated, de-identified counts of recurring code findings, MCP risk by category, and lab completion for the user's team over the last 30 days. No code or per-developer detail. Call this when a team lead asks how exposed the team is or which flaws keep recurring.

Input schema present but exposes no named parameters.

No output schema declared.

No examples provided.

Install

How do I install the MCPVetter MCP server?

MCPVetter runs locally as an npm package, launched with npx -y mcpvetter. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.

npm · mcpvetter

# add to Claude Code
claude mcp add com-mcpvetter-mcpvetter -- npx -y mcpvetter
Common questions

What is the MCPVetter MCP server?

MCPVetter is an MCP server listed in the public MCP registry as com.mcpvetter/mcpvetter. Audit your MCP servers for poisoned tools and leaked secrets, and check AI-generated code for flaws. This page covers its npm package (mcpvetter).

Is the MCPVetter MCP server safe to use?

MCPVetter scores 66 out of 100 on VerifyMCP. We recorded 1 known advisory against it as of 5 November 2026. It declares no install or post-install scripts. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.

What tools does the MCPVetter MCP server expose?

MCPVetter exposes 7 tools: review_code, explain_finding, secure_pattern, start_lab, check_lab, and 2 more. Their descriptions and schemas cost roughly 831 tokens of context every time the server is loaded.

Is the MCPVetter MCP server still maintained?

MCPVetter is still listed as active in the MCP registry. We last reached this channel on 5 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.

What licence is the MCPVetter MCP server under?

MCPVetter declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.