"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.

An MCP server that enhances sequential thinking with meta-cognitive capabilities including confidence tracking, hypothesis testing, and organized memory storage through graph-based libraries and structured JSON documents.

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

Enables AI assistants to solve linear, integer, mixed-integer, and knapsack optimization problems using Google OR-Tools via a simple JSON interface.

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.

Generate animation like 3blue1brown using a single prompt.

A demonstration implementation of Model Context Protocol (MCP) using NestJS framework, allowing developers to build MCP-compatible applications with playground testing capabilities.

Enables AI agents to perform safe sandboxed expression evaluation, symbolic calculus such as derivatives, simplification, equation solving and integration, statistical analysis, and JSON-native matrix operations. Everything is computed statelessly through the Model Context Protocol over stdio, HTTP, or SSE.

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.

An experimental server implementing the Model Context Protocol to allow AI assistants to query and analyze financial data stored in Beancount ledger files using the Beancount Query Language.

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.