"Official MCP (Model Context Protocol) Resources Only" matching MCP servers:

Shared troubleshooting memory and agent-to-agent communication for coding agents, enabling search of resolved problems, asking other agents when blocked, and checking replies.

A Model Context Protocol server that enables LLMs to read, search, and analyze code files with advanced caching and real-time file watching capabilities.

Sovereign, deterministic, local-first context and DAG lifecycle engine with native stdio MCP server for autonomous AI coding agents.

Enables coding agents to check human-stated assumptions against authoritative sources like AWS AppConfig, Sentry, and the repository, detecting when beliefs have drifted from reality. It provides tools to inspect belief history, list verifiable properties, review open drifts, and record verified understanding.

Share context, detect file conflicts, and send messages across parallel Claude Code sessions

Reduces token consumption for AI coding agents by 50-70% through intelligent code context filtering, Git delta tracking, and local SQLite/Tree-sitter indexing.

A token-efficient, codebase-aware AI agent runtime that provides MCP tools for starting runs, getting run states, explaining code, and finding files.

A local-first bridge for moving useful context between coding agents, indexing sessions and creating handoff packets for seamless agent switching.

MCP server to provide Jira Tickets information to AI coding agents like Cursor

Enables AI coding agents to save task context as a validated JSON document and later restore it as a concise briefing with the objective, status, relevant files and focus lines, decisions, and prioritized next steps.

Enables MCP clients like Claude Code to delegate coding tasks to a local model through a configurable coding-agent CLI backed by any OpenAI-compatible endpoint, with tools for health checks, task execution, and context-aware file modifications.

Persistent memory for AI coding agents. Store coding standards, architecture decisions, and project context across sessions with AES-256 encryption.

Enables coding agents to incrementally index project text and code, persist decisions and constraints with clear sources, and assemble focused project context across sessions via MCP.

Context compiler for AI coding agents that indexes TypeScript codebases to extract and serve only the relevant symbols and files for a task, reducing token usage and search overhead.

Enables coding agents like Cursor to access Figma design data, fetch layout and styling information, and implement designs in any framework.

An enhanced Model Context Protocol server that enables Claude to seamlessly collaborate with multiple AI models (Gemini, OpenAI, local models) for code analysis and development tasks, maintaining context across conversations.

Scoped, low-latency persistent memory layer for AI agents, Cursor, and Claude Code over Model Context Protocol with sub-50ms hybrid search.

This server lets MCP clients delegate coding tasks to a locally installed Qwen Code CLI, enabling second opinions, parallel work, and clean separation from the main context.

Lets your primary coding agent delegate grunt work to a cheaper model via OpenCode, enabling cost-effective task distribution.

Simple sequential thinking MCP in python