Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
作者提供的说明(英文原文)
MCP server for API testing. 35 tools: HTTP, assertions, flows, OpenAPI, collections.
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
你需要
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"api-testing": {
"command": "npx",
"args": [
"-y",
"@cocaxcode/api-testing-mcp"
]
}
}
}在终端中运行这条命令
claude mcp add api-testing -- npx -y @cocaxcode/api-testing-mcp粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"api-testing": {
"command": "npx",
"args": [
"-y",
"@cocaxcode/api-testing-mcp"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"api-testing": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@cocaxcode/api-testing-mcp"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。
你的选择通过 Cookie 保存在这台设备上,无需账户。
这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日
注册名称已通过 GitHub 账户 cocaxcode 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
它在你的电脑上运行;我们只能读取互联网端点的工具列表
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
GitHub 仓库最后更新于–,最新版本(0.11.12)于8个月前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。
同一分类中有:Spec Driven Development, Remote Desktop Commander, Tools MCP
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Web production tools for AI agents: contrast, JP text, WebP, JSON-LD, slugs, dates and 40+ more
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
@cocaxcode/api-testing-mcp
The most complete MCP server for API testing. Period.
42 MCP tools · Zero config · Works in any MCP client
Overview · Just Talk to It · Installation · Features · Tool Reference · Storage · Architecture
The most complete MCP server for API testing — 42 tools, zero config, nothing else comes close. This is not just a request sender. It is a full testing workbench: HTTP requests with assertions, multi-step flows with variable extraction, OpenAPI import with schema-aware mock data, load testing with percentile metrics, response diffing across environments, bulk test runners, reusable collections, environment groups with directory scoping and persistent defaults, Postman import/export, and cURL export. All from natural conversation. No accounts, no cloud, no generated files. Everything runs inline and stores as plain JSON you own.
You don't need to learn tool names or parameters. Describe what you want and the AI picks the right tool.
"Create a group called my-project and add this directory as scope"
"Set up a dev environment with BASE_URL http://localhost:3000" "Switch to prod for this session" "Set dev as the default environment" "Import my API spec from /api-docs-json" "Show me all user endpoints" "GET /users" "Create a user with random data" "Verify that DELETE /users/5 returns 204" "Login as admin, extract the token, then fetch dashboard stats" "How fast is /health with 50 concurrent requests?" "Run all my saved smoke tests" "Compare the users endpoint between dev and prod" "Export the create-user request as curl" "Export my collection to Postman"
If you've imported an OpenAPI spec, the AI already knows every endpoint, every required field, every valid enum value. When you say "create a blog post", it reads the schema and builds the request correctly — no guessing.
claude mcp add --scope user api-testing -- npx -y @cocaxcode/api-testing-mcp@latest
Add to your config file (~/Library/Application Support/Claude/claude_desktop_config.json on macOS, %APPDATA%\Claude\claude_desktop_config.json on Windows):
{
"mcpServers": { "api-testing": { "command": "npx", "args": ["-y", "@cocaxcode/api-testing-mcp@latest"] } } }
Add to .cursor/mcp.json or .windsurf/mcp.json in your project root:
{
"mcpServers": { "api-testing": { "command": "npx", "args": ["-y", "@cocaxcode/api-testing-mcp@latest"] } } }
VS Code / Codex CLI / Gemini CLI
VS Code — add to .vscode/mcp.json:
{
"servers": { "api-testing": { "command": "npx", "args": ["-y", "@cocaxcode/api-testing-mcp@latest"] } } }
Codex CLI (OpenAI):
codex mcp add api-testing -- npx -y @cocaxcode/api-testing-mcp@latest
Or add to ~/.codex/config.toml:
[mcp_servers.api-testing]
command = "npx" args = ["-y", "@cocaxcode/api-testing-mcp@latest"]
Gemini CLI — add to ~/.gemini/settings.json:
{
"mcpServers": { "api-testing": { "command": "npx", "args": ["-y", "@cocaxcode/api-testing-mcp@latest"] } } }
Once installed, set up an environment so relative paths resolve automatically:
"Create an environment called dev with BASE_URL http://localhost:3000"
If your API has a Swagger/OpenAPI spec, import it:
"Import my API spec from http://localhost:3000/api-docs-json"
Verify with: *"List my environments"* — you should see the one you just created.
Send any HTTP method with headers, query params, JSON body, auth, and {{variable}} interpolation. Relative URLs auto-resolve against BASE_URL.
"POST to /api/users with name Jane and email jane@company.com using my bearer token"
Supports: GET, POST, PUT, PATCH, DELETE, HEAD, OPTIONS — Bearer / API Key / Basic auth — custom timeouts.
AI agents pay for every byte that lands in their context window. By default, request now returns a compressed response that cuts 70-95% of those tokens without losing debugging value. Three optional parameters control it:
| Param | Values | What it does |
|---|---|---|
verbosity | 'minimal' / 'normal' (default) / 'full' | Controls detail level |
only_fields | ['user.id', 'items[*].name'] | Returns only these body paths (dot-notation + wildcards) |
max_body_bytes | number (default 2048) | Body size cap for 'normal' |
Modes:
status, timing, size_bytes, first 200 chars of body. Perfect for health checks, polling loops, or fire-and-forget calls. *Saves ~95% tokens.*Date, Server, CF-*, Set-Cookie, etc.) + body truncated to max_body_bytes. Covers ~80% of debugging use cases. *Saves ~75% tokens.*Typical savings on a 5 KB JSON response (≈1,500 tokens):
| Mode | Tokens consumed | Savings |
|---|---|---|
full | ~1,500 | 0% (baseline) |
normal | ~300-400 | ~75% |
minimal | ~50-80 | ~95% |
only_fields: ['data.id'] | ~30 | ~98% |
For a head-to-head comparison against
curl,WebFetchand other native alternatives with measured numbers, see Native alternatives below.
Recovering full responses: every compressed response includes a call_id. If you need the full body later, call inspect_last_response({ call_id }) — no need to re-execute the request. This works for request, assert, and each step of flow_run. Responses are kept in a 20-slot ring buffer and persisted to .api-testing/last-responses/ with a 1-hour TTL.
// Example: normal (default) response
{ "call_id": "k3m9a2xp", "status": 200, "statusText": "OK", "method": "GET", "url": "https://api.example.com/users/1", "timing": { "total_ms": 142 }, "size_bytes": 5324, "headers": { "content-type": "application/json" }, "body": { "id": 1, "email": "...", "...": "..." }, "body_truncated": true, "hint": "Body truncated to 2048 bytes (full size: 5324B). Call inspect_last_response({ call_id: \"k3m9a2xp\" }) for the full body.", "tokens_saved_estimate": 820 }
How this MCP compares against the native options Claude Code has when api-testing is not available (Bash + curl, WebFetch, etc.).
TL;DR: compared to raw curl, request saves between 65% and 97% of context tokens depending on the mode, with no loss of debugging information. Measured on a real call to GET /api/v1/blog returning 8 posts (~8.7 KB of JSON, 19 response headers):
| How the agent calls it | Uses MCP? | Tokens consumed | Delta vs curl |
|---|---|---|---|
Bash + curl (raw stdout) | ❌ native | ~2,170 | baseline |
WebFetch (LLM summary) | ❌ native | ~400-800 | −65%, but no auth / no envs / no inspect |
request verbosity=full | ✅ MCP | ~2,170 | 0% (same as curl, no compression) |
| `request` verbosity=`normal` *(default)* | ✅ MCP | ~750 | −65% |
request verbosity=minimal | ✅ MCP | ~50 | −97% |
request with only_fields: ["data[*].id","data[*].title"] | ✅ MCP | ~190 | −91% |
Why this table's numbers differ slightly from the "Compression modes" section above: these come from a single real-world response, while the previous table shows typical savings on a synthetic 5 KB response. Trend and order of magnitude are the same.
Notes:
normal) already saves 65% without any configuration: it filters out noisy headers (Date, Server, CF-*, Set-Cookie…) and caps the body at 2048 bytes.only_fields accepts dot-paths with array index and wildcard support (items[*].name) — returns only the fields you ask for.{{variable}} interpolation, stored environments, auth schemas, flows, Postman import/export, and inspect_last_response to recover the full body without re-hitting the server.Validate responses with structured pass/fail results:
"Verify that GET /api/health returns 200, body.status is ok, and responds in under 500ms"
PASS — 3/3 assertions passed
status === 200 body.status === "ok" timing.total_ms < 500
10 operators: eq, neq, gt, gte, lt, lte, contains, not_contains, exists, type
Chain requests with variable extraction between steps. Perfect for auth flows and CRUD sequences.
"Login as admin@test.com, extract the access token, then use it to fetch all users"
What the tool executes
flow_run({
steps: [ { name: "login", method: "POST", url: "/auth/login", body: { email: "admin@test.com", password: "SecurePass#99" }, extract: { "TOKEN": "body.access_token" } }, { name: "get-users", method: "GET", url: "/api/users", headers: { "Authorization": "Bearer {{TOKEN}}" } } ] })
Import specs from a URL or local file (JSON and YAML). Once imported, the AI knows every endpoint, parameter, and schema.
"Import my API spec from http://localhost:3000/api-docs-json"
"Import the spec from ./openapi.yaml" "What parameters does POST /users expect?"
Supports OpenAPI 3.x with full $ref resolution, allOf, oneOf, anyOf. OpenAPI 2.0 partially supported.
Generate realistic fake data from your OpenAPI schemas. Respects types, formats (email, uuid, date-time), enums, and required fields.
"Generate mock data for creating a user"
{
"email": "user42@example.com", "name": "Test User 73", "password": "TestPass123!", "role": "admin" }
Fire N concurrent requests and get performance metrics:
"How fast is the health endpoint with 50 concurrent requests?"
LOAD TEST — GET /api/health
Requests: 50 concurrent Successful: 50 | Failed: 0 Req/sec: 23.31
Min: 45ms | Avg: 187ms p50: 156ms | p95: 412ms | p99: 523ms Max: 567ms
Execute two requests and compare their responses field by field. Detect regressions or compare environments.
只显示了开头部分;其余内容请在仓库中阅读