#codex

An intelligent MCP server that serves as a guardian of development knowledge, providing Cline assistants with curated access to latest documentation and best practices across the software development landscape

An MCP server to communicated with, use, and wrap the API for the OpenAI Codex CLI tool.

A MCP server for calling MCP tools remotely without requiring installation.

An MCP server that scales development into controllable agentic, recursive flows, and build a feature from bottom-up

An MCP server to wrap the OpenAI Codex CLI for use with Claude Code

PubNub MCP Model Context Protocol Server for use in Cursor, Windsurf, Claude Desktop, Claude Code and OpenAI Codex and more!

Works with Claude Code, Claude Desktop, Cursor, OpenClaw, and any MCP-compatible client. The @latest tag ensures you always get the most recent version. Why Agents that integrate with external libraries are flying blind. They read docs (if they exist), guess at APIs, and hallucinate patterns that don't match the actual code. The result: broken integrations, wrong function signatures, outdated usage patterns, hours of debugging. When an agent can actually analyze the source code of a library or service it's integrating with, everything changes. It sees the real function signatures, the actual data flow, the patterns the maintainers intended. Integration becomes dramatically easier and less error-prone because the agent is working from ground truth, not guesses. What Agents Can Do With This - Integrate with any library correctly the first time — "How do I set up authentication with this SDK?" gets answered from the actual code, not outdated docs or training data. Your agent sees the real constructors, the real config options, the real error types. - Migrate between versions without the guesswork — Point your agent at both the old and new version of a library. It can diff the actual implementations and generate a migration plan that accounts for every breaking change. - Debug issues across repository boundaries — When a bug spans your code and a dependency, your agent can read both codebases and trace the issue to its root cause — even into libraries you've never opened. - Generate integration code that actually works — Instead of producing plausible-looking code that fails at runtime, your agent writes integration code based on the real API surface: actual method names, actual parameter types, actual return values. - Evaluate libraries before committing — "Should we use library A or B?" Your agent can analyze both implementations, compare their approaches to error handling, test coverage, and architectural quality, and give you a grounded recommendation. - Onboard to unfamiliar codebases in minutes — Point your agent at any repo and ask how things work. It answers from the code itself, with file paths and line numbers, not from memory that may be months out of date.

100% Rust implementation of code graphRAG with blazing fast AST+FastML parsing, surrealDB backend and advanced agentic code analysis tools through MCP for efficient code agent context management