"A library or package named 'pubmedmcp@0.1.3'" matching MCP servers:

MCP server enabling Claude Code to capture screenshots of Windows applications through WSL2, allowing AI to diagnose UI errors and verify layouts.

A validation layer for AI coding assistants that enforces explicit LLM evaluations on plans, code diffs, and tests to ensure safer and higher-quality code.

MCP-first A/B testing server that enables agents to manage experiments (create, update traffic splits, read results, apply winning variants) from tools like Claude Code, with self-hosted Cloudflare backend.

Zero-dependency MCP server and CLI that proxies, inspects, and analyzes JSON-RPC message streams between MCP clients and servers.

A shared, concurrency-safe ledger for LLM code audits. It enables multiple agents to collaboratively report, verify, and resolve bugs with deduplication and hallucination resistance.

Enables AI agents to drive the 1C:Enterprise test client directly, opening forms, reading and modifying data, working with tables, and recording or replaying action scenarios. Connects to local or remote test clients over their network protocol without a testing manager.

Enables agents to inspect web pages for Cloudflare Turnstile challenges without an API key, and clear Turnstile or WAF challenges using a SolveGate API key.

Agent Ready is an AI agent readability scanner — point it at any public URL and get back a 0–100 score plus per-check remediation hints for every failing check. This package wraps the same engine that powers agent-ready.dev as an MCP server, so Claude Desktop, Claude Code, Cursor, Cline, VS Code, and Windsurf can run scans inline and explain the results.

Enables AI agents to hire real human operators for tasks requiring physical presence, human perception, or judgment, such as verification, testing, data collection, and physical-world tasks.

Enables AI coding tools to run CheckMyLaunch scans on GitHub repos or live URLs and retrieve findings and repair prompts directly within the chat.

Enables AI agents to hire verified human operators for tasks requiring physical presence, human perception, or judgment, such as real-world verification, product testing, and data collection.

Visual CVE audit dashboard for npm, Python, Go, and Rust projects. Scans your project manifests (package-lock.json, requirements.txt, go.sum, Cargo.lock) against OSV.dev live data, opens a browser dashboard for human review, then applies fixes only after explicit confirmation. Supports multi-service monorepos in one command.

Translates a lockfile diff into a human-readable upgrade plan. For every dependency bump (npm or PyPI) returns semver class, breaking changes from GitHub release notes, CVEs fixed in the range, migration guide links, and a clear per-package recommendation. Bulk tool ranks up to 50 package changes in parallel by risk (security > caution > review > likely-safe > safe).

MCP server for verifying AI agent claims vs reality — single-transcript inline grounding-check that flags when an agent's response states facts not in the input context, when its code silently swallows exceptions and substitutes mock data, or when its multi-turn transcript contains contradictions or unverified completion claims. Sub-second, local, free, no API calls.

Enables an LLM or agent to drive a VUnit HDL unit-testing project end to end: list tests, compile, run, and inspect reports and per-test logs.

MCP server that exposes the JsonFabrica API as tools, so an AI agent can generate realistic synthetic JSON test data while it codes — single documents or full relational batches from reusable templates, reproducible with a seed. Runs locally over stdio; no network service.

Enables AI coding agents and IDEs to prove SystemVerilog assertions with SymbiYosys (smtbmc+z3) and lint SVA syntax, returning honest verdicts such as PROVEN, FAILED, UNKNOWN, ERROR, or TIMEOUT.

Grades MCP servers on agent usability (A–F) — description quality, schema design, tool naming and token cost, the properties that decide whether a model picks the right tool. Benchmarked across 36 popular servers; a third scored D or F.

Stops your AI from re-introducing bugs, leaking provider keys, or weakening tests. Bug fixes become permanent regression guards; blocked mistakes become AI lessons the agent reads and learns from before its next edit.

Render, analyze, and verify audio (WAV or FLAC) through a fully offline, deterministic engine, exposed as MCP tools for AI agents.