开源MCP工具 是 AI Skill Hub 本期精选MCP工具之一。综合评分 8.0 分,整体质量较高。我们强烈推荐将其纳入你的 AI 工具库,帮助提升工作效率。
开源MCP工具 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。
开源MCP工具 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。
# 方式一:通过 Claude Code CLI 一键安装
claude skill install https://github.com/IvoryCanvas/qamap
# 方式二:手动配置 claude_desktop_config.json
{
"mcpServers": {
"--mcp--": {
"command": "npx",
"args": ["-y", "qamap"]
}
}
}
# 配置文件位置
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%/Claude/claude_desktop_config.json
# 安装后在 Claude 对话中直接使用 # 示例: 用户: 请帮我用 开源MCP工具 执行以下任务... Claude: [自动调用 开源MCP工具 MCP 工具处理请求] # 查看可用工具列表 # 在 Claude 中输入:"列出所有可用的 MCP 工具"
// claude_desktop_config.json 配置示例
{
"mcpServers": {
"__mcp__": {
"command": "npx",
"args": ["-y", "qamap"],
"env": {
// "API_KEY": "your-api-key-here"
}
}
}
}
// 保存后重启 Claude Desktop 生效
Requires Node.js 20 or newer.
Run QAMap once without adding a dependency. Inside a repository whose default branch is origin/main (or main), the base is inferred automatically:
pnpm dlx @ivorycanvas/qamap qa
Pass --base <ref> --head <ref> for anything non-standard, and run bare qamap any time to see the start-here guide (qamap help prints the full reference).
That first command is intentionally manifest-free. It previews a PR comment/checklist that names the affected flow, recommended runner, draft file, missing fixture/selector/assertion evidence, and validation command.
Handing the result to a coding agent instead of a human? Use the compact agent format (see For Coding Agents):
pnpm dlx @ivorycanvas/qamap qa . --base origin/main --head HEAD --format agent
Install QAMap in a repository when you want a repeatable project command:
pnpm add -D @ivorycanvas/qamap
pnpm exec qamap qa . --base origin/main --head HEAD
Generate a Markdown artifact that an agent or reviewer can paste into a PR:
pnpm exec qamap qa . --base origin/main --head HEAD --output QAMAP_QA.md
When you are ready to create actual draft test files instead of a PR comment preview:
pnpm exec qamap e2e draft . --base origin/main --head HEAD --dry-run
pnpm exec qamap e2e draft . --base origin/main --head HEAD
Optional accuracy upgrade: create repo-local QA memory from the default branch and review it. Matching is anchor-path based today — a flow claims a PR only when changed files or routes hit its listed anchors — so add shared components to a flow's anchors when changes to them should map to that flow:
git switch main
pnpm exec qamap manifest context .
pnpm exec qamap manifest init .
git add .qamap/manifest.yaml
git commit -m "Add QAMap verification manifest"
Preview adoption without writing a manifest into the target repository:
pnpm exec qamap manifest init . --write /tmp/qamap-manifest.yaml
pnpm exec qamap qa . --manifest /tmp/qamap-manifest.yaml --base origin/main --head HEAD
Use the lower-level scanner when you want repository guardrail findings:
pnpm exec qamap scan .

This is a real, unedited recording against the published @ivorycanvas/qamap package on a small Next.js app with zero committed tests: qamap qa names the affected flow and the bootstrap plan, qamap e2e setup writes the Playwright config and starter spec, and npm run test:e2e finishes with 1 passed. First-run assertions are smoke checks — the point is a runnable starting point, not finished coverage.
Try the same loop on your own branch:
pnpm dlx @ivorycanvas/qamap qa . --base origin/main --head HEAD
QAMap reads the changed files and project signals:
Input
- git diff: origin/main...HEAD
- project structure: package.json, routes, test config, selectors
- optional team context: .qamap/manifest.yaml, CONTEXT.md, ADRs, goals, QA runbooks
Then it returns reviewable verification work:
Output
- PR comment/checklist draft for this branch
- changed domain language and candidate user flows
- recommended E2E runner or manual checklist
- draft Playwright, Maestro, CLI, API, or manual test files
- readiness status: runnable-candidate, near-runnable, or review-only
- blockers such as missing runner config, selectors, fixtures, or assertions
Trimmed real qamap qa output for a small Next.js notes-page change in a repository with no tests and no manifest (full output also lists validation gaps and a PR checklist):
```txt
Use qamap.config.json or .qamap.json to tune repository policy.
{
"$schema": "https://raw.githubusercontent.com/IvoryCanvas/qamap/main/schema/qamap.schema.json",
"failOn": "high",
"ignoreRules": ["QM011"],
"maxFiles": 2000,
"validationCommands": ["make test", "make lint"],
"severity": {
"QM007": "info"
}
}
See docs/configuration.md for details.
A local-first QA skill for AI-generated PRs. QAMap turns a PR diff into affected flows, missing evidence, and E2E drafts. No cloud. No LLM token.
QAMap is a local-first CLI that reads git changes, project structure, runner signals, selectors, and optional repo QA memory, then returns a PR-ready QA draft: which user flow may be affected, which runner fits, what E2E or checklist should exist, and what evidence is still missing before merge.
It is built for the moment when a reviewer asks: "This PR looks plausible, but which user flow could it break, and what should we test before merge?"
QAMap does not call an LLM API, upload source code, or require a service account. It runs in the repository you already have.
The core loop is intentionally simple:
PR diff
-> qamap qa
QAMap output
-> PR comment draft + E2E/checklist draft + missing evidence
Optional team memory
-> .qamap/manifest.yaml
-> better future PR recommendations
Local-first PR QA skill output. No cloud. No LLM token. Manifest is optional, not required for first use.
QAMap is intentionally small:
qamap qa turns a branch into a PR-ready affected-flow summary, suggested E2E/checklist draft, missing evidence list, and copyable checklistskills/qamap-pr-qa/SKILL.md gives coding agents a compact PR QA workflow for running QAMap before handoffIt is built for teams using AI coding agents, MCP-powered tools, or any workflow where an agent can read, edit, test, commit, or open pull requests.
For PR verification, QAMap treats the repository itself as the working base: committed manifests hold durable team language, ignored local history holds generated run observations, and the current branch diff supplies what changed now.
<details> <summary>한국어 소개</summary>
QAMap는 AI 코딩 에이전트가 만든 PR을 리뷰하기 전에 로컬에서 실행하는 QA 초안 CLI입니다.
PR diff와 repo 구조를 읽고 어떤 사용자 플로우가 영향받았는지, 어떤 E2E 또는 체크리스트가 필요한지, fixture/selector/assertion/runner/validation 근거 중 무엇이 부족한지 정리합니다. 클라우드나 LLM 토큰을 쓰지 않습니다.
pnpm dlx @ivorycanvas/qamap qa . --base origin/main --head HEAD
에이전트에게 넘길 때는 --format agent를 붙이면 같은 판단 내용을 약 2KB의 JSON으로 받을 수 있어, 매 세션 repo 탐색에 토큰을 쓰지 않아도 됩니다.
목표는 거대한 QA 플랫폼이 아니라, 유지보수자가 매번 에이전트에게 프로젝트 맥락과 검증 방법을 다시 설명하느라 쓰는 시간을 줄여주는 작고 선명한 도구입니다. Manifest 없이 바로 시작하고, 반복해서 틀리는 추천은 .qamap/manifest.yaml에 팀의 QA 언어로 보정해 향후 PR 추천을 개선합니다.
</details>
On a changed branch, QAMap tries to produce reviewable verification artifacts instead of only saying "write more tests":
.qamap/domains.yml, .qamap/flows.yml, and ignored .qamap/runs/ history so teams can improve the next run without spending LLM tokensThat means QAMap is most valuable when it becomes the team's verification base: humans define the durable language and critical flows once, QAMap reuses that base on each PR, and generated observations stay local unless the team intentionally promotes them into shared policy.
On the QA side, QAMap starts one step earlier than test-writing tools — it decides what a PR must prove before anyone records, generates, or writes a test:
| Tool category | Typical focus | QAMap focus |
|---|---|---|
| Test recorders and studios | Turning a known flow into a script by watching you run it. | Deciding which flow a PR affects and what evidence is missing, before recording starts. |
| LLM test generation | Spending model tokens to write test code from source. | Free, deterministic PR-to-QA mapping; drafts are starter scaffolds an agent or human finishes. |
| Re-prompting an agent per PR | Re-deriving repo QA context in every session. | Repo-owned QA memory (.qamap/manifest.yaml) plus a compact --format agent handoff. |
| Change-impact test selection | Choosing which existing unit/CI tests to run. | Naming the user-facing flow and E2E/checklist work that should exist at all. |
On the guardrails side, QAMap is not trying to replace the larger security ecosystem:
| Tool category | Typical focus | QAMap focus |
|---|---|---|
| OpenSSF Scorecard | Broad open source security posture. | AI-agent readiness at the repository boundary. |
| Secret scanning | Exposed credentials in code or history. | Secret-like values plus unsafe agent, workflow, and script context. |
| MCP security scanners | Deep analysis of MCP servers, tools, prompts, and skills. | Static repo checks without executing untrusted MCP servers. |
| General linters | Code style, correctness, or framework rules. | Guardrails that affect AI-assisted development safety. |
AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。
✅ MIT 协议 — 最宽松的开源协议之一,可自由商用、修改、分发,仅需保留版权声明。
经综合评估,开源MCP工具 在MCP工具赛道中表现稳健,质量优秀。如果你已有明确的使用需求,可以直接上手体验;如果还在评估阶段,建议对比同类工具后再做决策。
| 原始名称 | qamap |
| Topics | mcpaiclitypescript |
| GitHub | https://github.com/IvoryCanvas/qamap |
| License | MIT |
| 语言 | TypeScript |
收录时间:2026-07-05 · 更新时间:2026-07-05 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。
选择 Agent 类型,复制安装指令后粘贴到对应客户端