"Understanding and Using JSON" matching MCP servers:

Local MCP server that registers restricted Python filters and runs them against local JSON, YAML, and TXT files, enabling safe data filtering and file conversion.

A FastMCP server that turns JSON data into Plotly figures, deployable as an isolated service on Modal. Data goes in as JSON; a validated Plotly figure (JSON) or a rendered PNG comes back over the Model Context Protocol.

Enables AI assistants to run, list, and analyze Lupa test suites, returning structured JSON results for debugging.

An MCP server implementing AI-user confirmation protocols, providing tools for LLMs to seek user confirmation when uncertain through yes/no questions, action confirmations, intent clarification, understanding verification, and satisfaction ratings.

MCP server for agentic interaction with the Lean theorem prover via LSP, providing tools for understanding, analyzing, and interacting with Lean projects.

A lightweight server implementation that exposes Python functions as discoverable tools via HTTP using the Machine-to-Machine Communication Protocol (MCP). Enables remote execution of Python functions through a JSON-RPC interface with async support and type safety.

Provides AI assistants with deep semantic and runtime understanding of Jupyter notebooks.

An intelligent codebase processing server that provides agentic RAG capabilities for code repositories, enabling semantic search and contextual understanding through self-evaluating retrieval loops.

A boilerplate project for quickly developing Model Context Protocol (MCP) servers using TypeScript SDK, with example implementations of tools (calculator, greeting) and resources (server info).

A lightweight TypeScript-based MCP server that demonstrates how to build custom MCP tools by implementing a simple addition calculator. Serves as a starting point for building MCP-compatible tools.

Enables AI clients such as Claude Desktop, Cursor, and VS Code to connect via SSE and JSON-RPC, discover C# methods as MCP tools, and execute them with generated schemas. It also provides a Blazor WebAssembly inspector for monitoring and testing tool calls.

Provides MCP tools for agent health checks, step execution, and failure classification over Streamable HTTP JSON-RPC.

Enables AI assistants to query YashanDB using natural language through SQL operations.

A production-ready foundation template for building Model Context Protocol (MCP) servers with FastAPI, featuring modular tools, comprehensive testing, and OpenShift deployment configurations. Includes automated transformation scripts to create custom domain-specific MCP servers.

A server that enables AI assistants like Claude to safely run Python code and access websites, processing data for better AI understanding while providing helpful error messages.

An MCP server that provides a secure, sandboxed tool execution fabric for AI agents, featuring JSON-RPC 2.0 protocol support, read-only constraints, and an observability inspector dashboard.

A Python template for building Model Context Protocol (MCP) servers that expose tools via JSON-RPC, enabling secure and scalable context and tool invocation for language models.

A boilerplate project for quickly developing Model Context Protocol (MCP) servers using TypeScript SDK, with example implementations of calculator and greeting tools plus resource management capabilities.

Runs a language server and provides tools for communicating with it. Language servers excel at tasks that LLMs often struggle with, such as precisely understanding types, understanding relationships, and providing accurate symbol references.

A simple server that acts as a Master Control Program (MCP) for unified interaction with OpenAI and Anthropic (Claude) AI models through a single API endpoint.