"Understanding and Building Probability Density Functions (PDF) Models" matching MCP servers:

A zero-dependency Node.js playground for debugging how MCP clients authenticate to an MCP server over HTTP.

An MCP server that wraps the @apitomy/data-models library for querying, validating, and editing OpenAPI and AsyncAPI documents.

Enables LLMs to interact directly with generated REST APIs by exposing CRUD actions as MCP tools and read-only data as MCP resources.

Enables LLMs to discover and use generated REST API services as Model Context Protocol tools and resources, supporting CRUD actions and read-only data access without custom glue code.

Enables precise measurement of live HTTP and WebSocket services while maintaining an append-only, evidence-based ledger of claims that agents can query with explicit timestamps, staleness indicators, and instructions for re-verification.

Implements a secure MCP server with API Key and JWT authentication, providing tools like echo, login, secure_action, and admin_action. Includes MCP Inspector integration for testing and debugging.

This server facilitates scalable discovery and execution of OpenAPI endpoints using semantic search and high-performance processing, overcoming limitations of large spec handling for streamlined API interactions.

Enables agents to interact with the Logflare API by writing JavaScript functions that run in a Vercel Sandbox, using three tools: search, execute_read, and execute_write.

Turns an existing Express, Hono, Fastify, or Next.js API into a Streamable HTTP MCP endpoint, exposing one tool per OpenAPI operation that runs in-process through the app's existing routes, auth, validation, and handlers. It also serves discovery documents and readiness guidance so models can better understand and call the API.

Enables AI agents to create, run, and manage HTTP requests using the Invoker API client's .ivk format. It provides tools for scanning collections, executing requests, managing environments, and building documentation sites.

An MCP server that automatically discovers API endpoints from any codebase, generates and runs tests, and produces per-role QA audit reports in PDF and XLSX.

HAL (HTTP API Layer) is a Model Context Protocol (MCP) server that provides HTTP API capabilities to Large Language Models.

Provides OpenAPI validation and analysis tools to assist developers in building Model Context Protocol servers. It generates developer context and specific agent prompts based on API specifications to streamline the creation of agentic workflows.

Enables agents to price and analyze interest-rate, credit, equity and inflation trades by building validated requests to a QuantLib-based pricing engine and returning its numbers alongside the exact request sent. It also exposes curve construction from presets or pasted market data, risk analytics via finite differences, and reconciliation tools, while requiring user-supplied market data.

An MCP server that provides tools for exploring large OpenAPI schemas without loading entire schemas into LLM context. Perfect for discovering and analyzing endpoints, data models, and API structure efficiently.

An MCP server that connects to a Swagger specification and helps an AI to build all the required models to generate a MCP server for that service.

An MCP server that allows users to query and retrieve request and response JSON schemas directly from Swagger/OpenAPI specifications. It supports automatic reference resolution and path parameter matching to help AI models interact with API interfaces.

A standalone Model Context Protocol server that enables AI models to access the Australian Pharmaceutical Benefits Scheme (PBS) API data, including medicine listings, pricing, and availability information.

A Model Context Protocol server for executing GraphQL queries, allowing AI models to interact with GraphQL APIs through introspection and query execution.

This is an MCP server that facilitates building tools for interacting with various APIs and workflows, supporting Python-based development with potential for customizable prompts and user configurations.

An MCP server providing read-only access to Linear issues for language models, allowing them to fetch issue details and comments using a Linear API token.

Exposes REST API endpoints defined in an OpenAPI Specification as MCP tools, allowing AI models to call them via the ModelContext Protocol.

Enables MCP clients to connect over stateless Streamable HTTP or stdio, use an example time-zone tool with typed schemas and error handling, and validate deployed endpoints with a preflight checker.

Enables agents to discover and invoke operations from any OpenAPI service by managing deployments, authentication, and request building.

Exposes add, subtract, multiply, and divide tools for performing arithmetic operations over stdio via the Model Context Protocol.

Exposes FastAPI routes as MCP tools with auto-discovery, Pydantic schema extraction, and built-in observability.

MCP server for loading and exploring OpenAPI/Swagger specifications, enabling AI assistants to dynamically browse API contracts by loading specs, searching endpoints, inspecting schemas, and retrieving operations.

A TypeScript-based MCP server that provides backend API handling and facilitates communication between microservices. Features an organized structure with controllers, routes, and models for easy extensibility and maintenance.

A bridge that exposes any GraphQL API as tools consumable by Large Language Models via the Model Context Protocol (MCP), automatically discovering and translating API capabilities with zero maintenance required.

A template for building Model Context Protocol servers that connect to company REST APIs using FastMCP, providing authentication handling, error management, and example tools for common API operations.