JitAPI provides just-in-time API orchestration for language models by dynamically discovering relevant API endpoints from OpenAPI specifications. It simplifies API interactions using semantic search and dependency graphs, ensuring optimized workflows without unnecessary complexity. Perfect for those looking to enhance LLM capabilities.
JitAPI: Just-in-Time API Orchestration for LLMs
JitAPI is an advanced MCP server designed for seamless interaction between large language models (LLMs), such as Claude, and various APIs. By dynamically discovering relevant endpoints from OpenAPI specifications, JitAPI streamlines the API interaction process without the need to load entire API specifications into memory. Instead, it leverages semantic search and dependency graphs to pinpoint only the endpoints essential for specific tasks.
Utilizing JitAPI allows users to interact with APIs in a conversational manner. For instance:
User: "Register the Petstore API from https://petstore.swagger.io/v2/swagger.json"
Claude: [calls jitapi:register_api tool]
✓ Registered Swagger Petstore with 20 endpoints
User: "Find a pet and get its details"
Claude: [calls jitapi:get_workflow tool]
Workflow planned:
1. GET /pet/findByStatus - Find pets by status
2. GET /pet/{petId} - Get pet details
Parameters extracted: status="available" (from context)
User: "Execute that workflow"
Claude: [calls jitapi:set_api_auth, then jitapi:execute_workflow]
Step 1: Found 3 available pets
Step 2: Retrieved details for pet "Max"
JitAPI employs a sophisticated architecture split into two primary pipelines:
To function effectively, JitAPI necessitates an OpenAI API key for embeddings and workflow planning. This key can be set through various configuration options including MCP settings, environment variables, or a .env file located in specified directories.
JitAPI supports multiple authentication methods such as API Key in headers, query strings, and Bearer tokens to ensure secure API calls.
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