MCP.Market

Jitapi MCP server

by nk3750·io.github.nk3750/jitapi·v0.1.1·7 stars

Turn any OpenAPI spec into an executable agent interface using graphs and semantic search.

B72/100grade B
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Safety scan
B72/100

full report

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7 stars

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Jitapi tools

No tool declarations could be read from the package source. They show once the server is installed.

Public scan report

scanner v0.1.10 · 2026-10-01 · same rubric, same numbers if you re-run it

no findings
  • Code scan29 source files scanned25/25
  • –Live reliabilityno gateway calls yet and no remote to proben/a
  • –Tool poisoningtools not inspected (local package is not executed); not countedn/a
  • Auth qualitystatic API keys via environment variables6/15
  • Maintenancelast push 109 days ago8/15
  • Maintainer identityregistry namespace matches repository owner; GitHub account older than a year8/10
Overall 72/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

How Jitapi compares

Against every graded AI and agents MCP server, from our own scans and uptime checks. Updated after each scan run.

Safety score
72/100
higher than 40% of 4,460 AI and agents servers
GitHub stars
7
more than 81% of 2,711 AI and agents servers

Install Jitapi in Claude Code, Cursor or VS Code

claude mcp add jitapi -- uvx jitapi
Add to Cursor

What the publisher says

From the Jitapi repository's README, as published. We do not edit it. Read it on GitHub

JitAPI

Point Claude at any API. JitAPI figures out which endpoints to call and in what order — automatically.

<!-- mcp-name: io.github.nk3750/jitapi -->

JitAPI is an MCP server that lets Claude interact with any API from its OpenAPI spec. Instead of dumping hundreds of endpoints into context, JitAPI uses semantic search and a dependency graph to surface only what's needed — then Claude plans and executes the calls.

https://github.com/user-attachments/assets/53f72f89-a41a-4a9c-a688-ec876ea05fbd

The Problem

Stripe has 300+ endpoints. GitHub has 800+. Loading the full spec into Claude's context wastes tokens and causes hallucinations. Writing a custom MCP server for every API you use doesn't scale.

JitAPI solves this: register any OpenAPI spec once, then ask for what you need in plain English. It finds the right endpoints, resolves dependencies between them, and lets Claude execute the calls.

Quick Start

pip install jitapi

Add to your Claude Code config (.mcp.json):

{
  "mcpServers": {
    "jitapi": {
      "command": "uvx",
      "args": ["jitapi"]
    }
  }
}

That's it. No API keys required — JitAPI uses local embeddings out of the box.

Then in Claude:

You: Register the GitHub API from https://raw.githubusercontent.com/github/rest-api-description/main/descriptions/api.github.com/api.github.com.json

Claude: ✓ Registered GitHub v3 REST API — 1,107 endpoints indexed

You: List my repos

Claude: [searches for "list repositories for authenticated user" → finds GET /user/repos → executes]
Here are your repositories: ...

Multi-API Orchestration

The killer feature: register multiple APIs and ask questions that span them. JitAPI searches across all registered APIs and Claude chains the calls.

You: Register the TMDB API and OpenWeatherMap API
Claude: ✓ Registered both APIs

You: Find the top popular movie on TMDB, then get the weather where it was filmed

Claude: [searches TMDB → GET /movie/popular → GET /movie/{id} for production locations
         → searches OpenWeather → GET /data/2.5/weather with the city]

The #1 popular movie is "Inception", filmed in Los Angeles.
Current weather in LA: 72°F, partly cloudy.

How It Works

Register API                          Ask a question
     │                                      │
     ▼                                      ▼
Parse OpenAPI spec               Embed query → vector search
     │                                      │
     ▼                                      ▼
Build dependency graph           Find relevant endpoints
     │                                      │
     ▼                                      ▼
Embed all endpoints              Expand with dependencies
     │                                      │
     ▼                                      ▼
Store in vector DB               Return schemas → Claude executes
  1. Register — Parse an OpenAPI spec, build a dependency graph (which endpoints need data from which other endpoints), and create searchable embeddings for all endpoints
  2. Search — When you ask a question, JitAPI embeds your query and finds the most relevant endpoints via cosine similarity
  3. Expand — The dependency graph adds any prerequisite endpoints (e.g., "you need to call GET /users first to get the user_id for POST /orders")
  4. Execute — Claude gets the endpoint schemas and makes the API calls, passing data between steps

MCP Tools

Shortened. The full README is on GitHub.

Nothing above is checked by us. What we check is on the safety report.

Jitapi: common questions

Is Jitapi MCP server safe?
Mostly: it is graded B (72/100). Read the Jitapi safety report
How do I install Jitapi?
It runs on your machine. Copy the Claude Code, Cursor, VS Code or Claude Desktop config from the install section.
Does Jitapi need an API key?
Yes. The registry entry asks for OPENAI_API_KEY.
Is Jitapi maintained?
The last commit was 143 days ago (2026-06-14). The latest release is v0.1.1.
What can I use instead of Jitapi?
Servers from other publishers that do the same job: Serena MCP: the IDE for your agent MCP server, Power BI Authoring MCP Server (Local) and Memanto MCP server.
How does Jitapi compare with other AI and agents MCP servers?
safety score 72/100 (higher than 40% of 4,460 AI and agents servers); github stars 7 (more than 81% of 2,711 AI and agents servers). Full comparison.

Alternatives to Jitapi

Same job from other publishers: the closest match first, then the best rated.

  • Serena MCP: the IDE for your agent
    A powerful toolkit for coding, providing semantic retrieval and editing capabilities.
    C
  • Power BI Authoring MCP Server (Local)
    The Power BI Authoring MCP server lets an AI agent create and change Power BI semantic models.
    A
  • Memanto
    MCP server for Memanto - persistent semantic memory for any MCP-compatible agent
    A
  • MCP AutoMem
    Graph-vector memory for AI assistants using FalkorDB and Qdrant
    C
  • Total Agent Memory
    Persistent local memory for coding agents: temporal knowledge graph, procedural and episodic recall
    A