"Debugging and Navigating Code in a Project" matching MCP servers:

A self-hosted remote MCP server for cross-model AI session handoff, allowing users to start a project in one AI assistant and continue in another via shared project logs, files with version history, and full-text search, all stored in a single SQLite file on their own server.

Enables Codex to rank available tools and skills, rerank code and documentation search results, and triage saved output artifacts using Jev-based relevance scoring while leaving final decisions to Codex.

An MCP relay that adds a use_jev tool to every connected server, letting the Jev model autonomously select and execute tool calls while the calling model supplies any required text values. It enables agents to delegate multi-step tool use across all configured MCP servers through a single unified interface.

Enables Codex to manage a local Claude Code companion through MCP, implementing a dual-agent workflow where Codex handles reasoning and review while Claude Code performs engineering tasks.

Enables coding agents in different editors and MCP clients to share messages, project context, notes, task ownership, handoffs, and advisory file claims through a local SQLite-backed coordination server. Supports MCP stdio, MCP Streamable HTTP, a JSON HTTP API, and a local CLI for multi-project agent collaboration.

Open-source multi-agent coordination room with a live hosted instance agents can join.

Enables AI coding agents to request a second opinion from a different frontier model via a single tool call, with PII scrubbed before transit, live web search, and support for Claude, ChatGPT, Cursor, and Gemini.

Enables AI agents to request an independent second opinion from a different frontier model, with PII scrubbing before transit, live web search grounding, caching, and multi-provider failover.

Enables users to describe an LLM fine-tuning workload once and receive ranked, task-aware GPU recommendations across a dozen cloud providers based on required VRAM/FLOPs, cost, and runtime. It surfaces the cheapest, fastest, and balanced options through MCP so coding assistants can plan training hardware without manual price comparison.

Enables multi-agent council workflows for coding assistants, providing parallel independent reviews, plans, brainstorms, validation, and research with strict read-only boundaries.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

Installs once in Claude Code and automatically discovers the nearest project's mcp.json, launching that checkout's declared MCP servers and forwarding their tools, resources, and prompts through a single connection. Each session keeps its own isolated connections, so repositories can ship their own local server manifests without maintaining a global configuration.

Provides access to a library of 18 specialized skills, project templates, and prompt patterns for Claude Code. It enables automated workflows for feature development, security audits, and system maintenance through multi-agent collaboration.

MCP server that enables AI assistants to run multi-step agent pipelines (e.g., Issue Analyst → Code Writer → Test Runner → PR Opener) from conversations, with support for Devin, shell, Python, and HTTP agents.

Enables AI agents and human managers to orchestrate GitHub-based project work by self-assigning tasks, coordinating swarms, tracking token budgets, generating PRDs, planning sprints, triaging issues, and materializing tasks as GitHub issues, milestones, and project views.

Enables any MCP-capable coding agent to share one plain-Markdown project state — goals, tasks, decision logs, and session handoffs — stored in a project's .apm/ directory, with lock-safe concurrent writes and consistency checks that flag stale, blocked, or unowned work. This lets multiple agents and a human CLI resume work across interrupted sessions without a database or external service.

Turns GitHub Projects v2 into an agentic project management layer where AI coding agents self-assign tasks, coordinate via heartbeats, submit work for review, and stay within token budgets. It also provides AI-powered PRD generation, sprint planning, issue triage, and a PRD-to-issues pipeline, all backed by GitHub-native storage with no external infrastructure.

Turns GitHub Projects v2 into an agentic project-management layer where AI coding agents can register, claim tasks, coordinate, submit work for review, and enforce token budgets, while humans can generate PRDs, plan sprints, triage issues, and create roadmaps using GitHub-native storage.

Turns GitHub Projects v2 into an agentic project-management layer where AI coding agents register, atomically claim and self-assign tasks, heartbeat, review each other's work products, and operate within token budgets. It also exposes 20 compound tools (169 actions) plus MCP resources, prompts, and sampling for AI-powered PRD generation, task breakdown, sprint planning, issue triage, and a PRD-to-issues pipeline, all stored natively in GitHub with no external infrastructure.

Enables AI coding agents and project managers to turn GitHub Projects v2 into an autonomous project management layer with task checkout, multi-agent swarms, PRD-to-issue pipelines, sprint planning, triage, and GitHub Actions/Releases/Branches support via 20 compound tools and 169 actions.