"An overview of knowledge graphs and their applications" matching MCP servers:

A meta-server that aggregates multiple MCP servers into a single interface, reducing token usage by 98%+ through progressive tool discovery and direct code execution that processes data between tools without consuming context window space.

Chain of Draft Server is a powerful AI-driven tool that helps developers make better decisions through systematic, iterative refinement of thoughts and designs. It integrates seamlessly with popular AI agents and provides a structured approach to reasoning, API design, architecture decisions, code r

An MCP server that uses Groq's API to expose raw chain-of-thought tokens from Qwen's qwq model, enabling LLMs to think step-by-step before responding.

An MCP server implementation that helps Claude remember and learn from programming errors by storing, retrieving, and finding similar errors with full context.

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.

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.

Enables running one Codex CLI task at a time in an isolated temporary folder, returning Codex's answer and a manifest of produced files for manual review and selective copying.

Provides an isolated workspace for testing candidate code, runs tests, and returns deterministic pass/fail verdicts. Enables automated grading of software engineering solutions by ensuring reproducible test runs.

An MCP server that exposes the Prova reasoning verifier, enabling AI agents to verify their own reasoning and kernel-check Lean 4 proofs before outputting answers.

Enables serving a directory of Agent Skills to MCP clients, listing available skills, delivering their instructions and bundled files, and running only the scripts each skill explicitly declares under strict execution limits.

An MCP server that enables AI applications to access 20+ model providers (including OpenAI, Anthropic, Google) through a unified interface for text and image generation.

Analyzes codebases to generate dependency graphs and architectural insights across multiple programming languages, helping developers understand code structure and validate against architectural rules.

Enables AI coding agents to compile, test, run, hot-swap, and debug Java applications incrementally through MCP, providing runtime feedback for verifying code changes.

Enables AI agents to autonomously write, run, and debug NASA GMAT mission scripts, with tools for script execution, curated knowledge of common pitfalls, and semantic documentation search.

A bridge server that provides access to an Odoo shell environment, allowing execution of Python code within an Odoo database context for model introspection and database operations.

Manages a Neo4j knowledge graph of reusable Python functions, documentation, and environment variables, enabling dynamic script composition and execution with automatic dependency management and secret protection.

Enables agents to run heavy computation—heat-equation solves, matrix exponentials, linear algebra, FFTs, compression, and arbitrary dispatched workloads—on their own machine while routing jobs through an automatic chooser and returning verifiable result digests with CPU-seconds and credit accounting.

An MCP server for LaTeX that enables AI assistants to check documents for errors, parse compiler logs, and compile LaTeX files, helping them fix documents instead of guessing.

Provides a set of read-only Kubernetes functions via an MCP server, enabling interaction with Kubernetes clusters through agents or coding assistants like GitHub Copilot.

Enables AI assistants to see, control, and debug PySide6 desktop applications without modifying their source code, by capturing screenshots, inspecting widget trees, performing clicks and typing, reading logs, and running Python inside the app process.