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CommunicationAuto-generatedScore: 46

APIs.guru MCP Server

APIs.

Quick Start Summary

The APIs.guru MCP server is a Model Context Protocol bridge that connects AI assistants — including Claude Desktop, Cursor, Windsurf, and VS Code Copilot — to the APIs.guru API through natural language. It exposes 7 API endpoints as callable tools, such as List all APIs, Get basic metrics, List all providers, and more. No authentication is required — setup takes approximately 30 seconds. The server uses STDIO transport and can be installed by running npx -y @mcp/apis-guru. This integration is sourced from the auto APIs.guru OpenAPI specification (v2.2.0) and has a quality score of 46/99 (fair documentation coverage).

7Endpointstools mapped
NoneAuthopen access
46/99Qualityfair
~30 secSetupno auth

Server Details

Category
Communication
Authentication
None
Endpoints
7 operations
Transport
STDIO
Spec Version
v2.2.0
Install Command
npx -y @mcp/apis-guru

Environment Variables

APIS_GURU_API_KEY

Example: your_apis_guru_api_key

Top Endpoints

GET
/list.json

List all APIs

GET
/metrics.json

Get basic metrics

GET
/providers.json

List all providers

GET
/specs/{provider}/{api}.json

Retrieve one version of a particular API

GET
/specs/{provider}/{service}/{api}.json

Retrieve one version of a particular API with a serviceName.

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📖 Detailed MCP Integration Guide

A technical breakdown of capabilities, agent workflows, and security/configuration best practices.

Capabilities & Use Cases
APIs.guru serves as a centralized, authoritative index for the web API ecosystem, functioning as a dynamic encyclopedia of machine-readable API definitions. Maintained by a dedicated community and governed by a transparent process, its core capability is to aggregate, standardize, and serve the latest OpenAPI specifications from thousands of public APIs. By providing a single, unified interface for these diverse API contracts, it eliminates the significant overhead developers traditionally face in manually searching for, validating, and integrating API documentation from disparate sources. The platform is invaluable for enterprises and individual developers engaged in building integrations, developing API-driven applications, conducting market research on available services, or ensuring their own API adheres to community standards. Typical use cases span from a startup needing to quickly discover and compare payment processing APIs to a large corporation auditing its API portfolio for consistency and identifying opportunities for consolidation or enhancement.
🤖AI Agent Value
Exposing the APIs.guru repository as tools via the Model Context Protocol (MCP) transforms it from a passive reference into an active, intelligent resource for AI coding assistants. This integration grants the AI agent a live, queryable awareness of the vast API landscape, enabling it to move beyond static code generation to context-aware, informed decision-making. The primary value lies in automated discovery and validation. Instead of a developer manually looking up an API's schema, the AI can programmatically fetch and analyze the precise, current specification needed for a task. This dramatically accelerates prototyping, reduces integration errors, and ensures generated code or configurations are built upon the correct, versioned contract. For the AI assistant, this MCP server becomes a critical tool for answering queries about API compatibility, identifying the best-fit service for a described need, and scaffolding client code that is immediately accurate.
💬Example Workflows
A developer can instruct the AI coding assistant to perform a wide array of dynamic tasks leveraging this MCP connection. For instance, a developer could ask: "Find the official OpenAPI spec for the Stripe Payments API and generate a TypeScript client with methods for creating charges and refunds." The AI would use the tools to locate the precise /specs/{provider}/{api}.json endpoint, fetch the specification, and generate code aligned with its exact endpoints, parameters, and schemas. Another powerful workflow involves comparative analysis: "List all APIs from the 'github' provider and identify which ones support webhooks for repository events." The AI would query /providers.json to confirm the provider, then iterate through available APIs using /list.json or provider-specific endpoints to analyze each spec's webhooks section. This enables automated audits, dependency checks, and intelligent recommendation engines, where the AI agent can state, "Based on the latest specs in APIs.guru, the 'aws-lambda' API supports the following authentication methods..." enabling the developer to make informed choices immediately.
🛡️Security & Auth
While the APIs.guru API itself requires no authentication for public access, developers and AI agents must adhere to responsible consumption practices. The most critical guideline is to respect service availability through rate limiting and efficient caching. Since the data is public but dynamically generated, excessive or naive polling can impact performance. Best practices include caching responses locally, using conditional requests where applicable, and implementing exponential backoff for retries. From a security and principle-of-least-privilege perspective, even though the API is open, the AI agent's tool should be configured with a narrow scope—only querying for data it explicitly needs for the current task rather than performing broad, exploratory crawls. When setting up the MCP server, developers should ensure their client configuration explicitly lists only the required endpoints (like GET /specs/...) to minimize the agent's operational surface area and focus its capabilities on precise, task-oriented data retrieval.

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