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"Proof-of-concept examples for Multi-Component Processes (MCPs)" matching MCP servers:

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    Aggregates multiple third-party APIs into unified MCP tools, providing out-of-the-box access to 10 popular services including OpenWeather, Google Maps, GitHub, Notion, Spotify, and more. Enables users to interact with weather data, search places, manage repositories, create content, and access various web services through a single MCP server.
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    MIT
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    Enables AI assistants to interact with the Post For Me API, allowing them to explore endpoints, make test requests, and use documentation to help integrate this SDK into your application.
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    Apache 2.0
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    MCP server for Akamai APIs. 198 tools covering Property Manager, Edge DNS, CPS, WAF, and reporting. Built with TypeScript, featuring modular architecture, comprehensive testing, and multi-account support. Make Akamai accessible to AI assistants.
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    Enables AI to interact with multiple Swagger/OpenAPI specifications by listing endpoints, exploring models, and generating TypeScript code. It centralizes the management of various APIs through a configuration file, allowing for seamless integration of documentation into AI workflows.
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    MIT
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    A proof-of-concept MCP server implementing OAuth 2.1 authorization with CIMD client registration and PKCE, demonstrating protected resource access and step-up authentication.
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    Turn any OpenAPI 3.x spec into a runnable, stateful API environment for AI agents. Test real integration flows — multi-step workflows, persistent state, webhook delivery, retries, and edge cases — instead of guessing from docs or mocking endpoints. Generate committable markdown reports directly from Claude/Cursor. Includes 50+ pre-validated APIs like Stripe, GitHub, Twilio, OpenAI, and more.
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    MIT
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    TrustPilot for APIs, built for AI agents. Independent reliability ratings for APIs and MCP servers — look up trust scores, compare providers side by side, and leave reviews from real agent traffic.
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    Optimizes REST API calls for AI agents by returning compact schemas instead of full responses, allowing agents to fetch only needed fields via jq masks, reducing token consumption by up to 50x.
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    MIT
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    A TypeScript-based MCP server that enables testing of REST APIs through Cline. This tool allows you to test and interact with any REST API endpoints directly from your development environment.
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    MIT
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    ozon-mcp is a knowledge-rich MCP server that turns the entire Ozon seller toolkit into 15 high-leverage tools. AI agents (Claude, Cursor, Cline, Continue, Goose, Zed, …) can search the API in Russian or English, drill into any of 466 methods with a fully-resolved JSON Schema, and execute calls with built-in safety guards. Subscription- aware, automatic pagination over all 4 cursor styles, retry/ba
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    MIT
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    Probes your live API and classifies why each endpoint failed (root cause, evidence, and a calibrated confidence level), exposed over MCP so your AI assistant debugs from evidence instead of guessing. Works with FastAPI, Express, Next.js, tRPC, and GraphQL.
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    Apache 2.0
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    A high-performance, multi-protocol API mock server with 18 built-in MCP tools for managing HTTP, GraphQL, gRPC, WebSocket, MQTT, SSE, and SOAP mocks, chaos engineering, stateful CRUD resources, and request verification.
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    Apache 2.0
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    An MCP server for Tern that enables the generation, verification, and management of webhook handlers across over 16 platforms and various frameworks. It provides tools to create verified endpoints, debug signature failures, and manage failed event replays via dead letter queues.
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    MIT
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    Enables AI assistants to interact with Fastly's CDN API through the Model Context Protocol, allowing secure management of CDN services, caching, security settings, and performance monitoring without exposing API keys.
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  • A
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    CLIRank - API discovery for AI agents. APIs scored on CLI relevance: auth method, JSON responses, headless operation, pricing transparency. Search by capability, compare alternatives, read agent-contributed docs. 6 MCP tools for discovery, comparison, and documentation. Agents contribute observations back - crowdsourced API docs by machines, for machines.
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    130
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    MIT