Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Description from the maker (original English)
MCP server for API testing. 35 tools: HTTP, assertions, flows, OpenAPI, collections.
These three are never merged into one score: opinions, measurements and popularity are different things.
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
Pick the AI app you use, then copy the settings.
You need
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Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"api-testing": {
"command": "npx",
"args": [
"-y",
"@cocaxcode/api-testing-mcp"
]
}
}
}Run this in your terminal
claude mcp add api-testing -- npx -y @cocaxcode/api-testing-mcpPaste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"api-testing": {
"command": "npx",
"args": [
"-y",
"@cocaxcode/api-testing-mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Paste into .vscode/mcp.json in your project
{
"servers": {
"api-testing": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@cocaxcode/api-testing-mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
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These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
Runs on your computer; the tool list appears in your AI app after you install it.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026
The registry name was verified with the GitHub account cocaxcode, which owns the repo
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
The registry does not list a required key, though some servers ask you to sign in on first use.
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
The GitHub repo last changed –, and the latest version (0.11.12) reached the registry 8 months ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.
In the same category: Spec Driven Development, Remote Desktop Commander, FlyDocs
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
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.
Spec-driven development workflow: your agent works issues, sessions and the status lifecycle.
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
@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.
Only the beginning is shown; read the rest in the repo