Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
作者による説明(英語の原文)
MCP server for API testing. 35 tools: HTTP, assertions, flows, OpenAPI, collections.
この3つを1つのスコアにまとめることはしません。意見、測定、人気はそれぞれ別のものだからです。
このサーバーのやさしい解説はまだ書いていません。以下はレジストリから読み取った事実です。
使っているAIアプリを選んで、設定をコピーしてください。
必要なもの
どのアプリを使っていますか?
上でアプリを選んでください。選んだアプリを覚えておくので、次からはボタンに「…にインストール」と表示されます。
Claude Desktop → Settings → Developer → Edit Config を開き、claude_desktop_config.json にこれを貼り付けます
{
"mcpServers": {
"api-testing": {
"command": "npx",
"args": [
"-y",
"@cocaxcode/api-testing-mcp"
]
}
}
}ターミナルでこれを実行します
claude mcp add api-testing -- npx -y @cocaxcode/api-testing-mcpプロジェクトの .cursor/mcp.json、または ~/.cursor/mcp.json に貼り付けます
{
"mcpServers": {
"api-testing": {
"command": "npx",
"args": [
"-y",
"@cocaxcode/api-testing-mcp"
]
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
プロジェクトの .vscode/mcp.json に貼り付けます
{
"servers": {
"api-testing": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@cocaxcode/api-testing-mcp"
]
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
これはパソコンで動くサーバーですが、ChatGPTがつなげるのはインターネット上のサーバーだけです。ほかのアプリを選んでください。
選んだアプリは、この端末のCookieに保存されます。アカウントは不要です。
この設定は、作者のレジストリ登録情報から作成しています。READMEと違う場合は、READMEに従ってください。
パソコンで動くサーバーです。インストールすると、ツール一覧がAIアプリに表示されます。
誰でも見られる情報をチェックしており、コードは読んでいません。当てはまらない項目は数に入れていません。 · 2026年11月6日にチェック
レジストリ名は、リポジトリを所有するGitHubアカウント cocaxcode で確認済みです
このリポジトリはまだGitHubから読み取っていません
このリポジトリはまだGitHubから読み取っていません
これはパソコンで動くため、インターネット上のエンドポイントはありません
パソコンで動き、代わりにローカルの設定を使います
パソコンで動くサーバーです。ツール一覧を読み取れるのは、インターネット上のエンドポイントがある場合だけです
レジストリでこのカテゴリにある有効なすべてのサーバーと比較しています。スターは、公開者がリポジトリの所有者である場合だけ数えます。
当てはまる基本チェック1項目のうち1項目に合格しました。コードは読んでいません。キーやトークンを追加する前に、サーバーがどんなアクセスを求めているかを確認し、必要な権限だけを持つキーを使ってください。
レジストリには必須のキーは載っていません。ただし、最初に使うときにサインインを求めるサーバーもあります。
あなたのパソコンで動きます。必要なときにAIアプリが起動し、データは作者のサーバーを通りません(サーバー自体がオンラインサービスを呼び出す場合を除きます)。
GitHubリポジトリの最終更新は–、最新バージョン(0.11.12)がレジストリに届いたのは8 か月前です。
Claude Desktop、Claude Code、Cursor、VS CodeなどのMCP対応アプリで使えます。パソコンにNode.jsも必要です。
同じカテゴリには:Spec Driven Development, Remote Desktop Commander, Recta
同じカテゴリで、ほかの作者が作ったサーバー。スターの多い順。
カードの見方:★ GitHubスターi3つの柱:レビュー · チェック · 利用状況i更新=最新バージョンをレジストリに公開した日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.
Cheaper cross-chain stablecoin routes for AI agents; 5 % of the verified saving, nothing if none.
作者のリポジトリから書かれたとおりにコピーしたものです。PopNx MCPは内容を確認・推奨していません。HTML、画像、スクリプトは取り除いています。リンクはこのサイトの外へ移動します。
@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.
冒頭だけを表示しています。続きはリポジトリで読めます