"Using Cline as a Knowledge Graph for Coding" matching MCP servers:

Exposes Cline CLI session history as read-only resources and provides asynchronous tools for Hermes Agent to monitor, analyze, start, resume, and terminate Cline worker processes.

Run DeepSeek as a real sub-agent inside Claude Code / Codex CLI — not just a single LLM call. DeepSeek gets its own 7-tool agent loop (Read/Write/Edit/Bash/Glob/Grep/NotebookEdit) inside a sandboxed workspace.

Enables Codex to delegate review and implementation tasks to the local Qoder CN CLI as background jobs, with two permission tiers and polling-based status updates.

Exposes the local Claude Code CLI as a subagent for Codex, enabling background review and implementation jobs with two fixed permission tiers.

Delegates work from MCP clients (like Claude Code) to the Codex CLI, allowing spawning of autonomous Codex subagents for tasks.

Enables MCP clients to run Cline CLI prompts and isolated repository-wide analyses, returning terminal text responses with model, duration, and usage/cost.

Enables external AI clients to invoke an autonomous coding agent that plans, fixes, and self-corrects code in an isolated Docker sandbox via run_task and run_tests tools.

Exposes a running DeepSeek Harness as an MCP endpoint so any other agent or script can create workspaces and sessions, hand coding tasks to the DSH agent, read and write files, run shell commands, and invoke permitted harness tools under the same sandbox, transcript, and permission policy as the DSH UI.

Enables dispatching entire autonomous multi-agent engineering swarms directly from IDEs like Cursor and Windsurf via a single MCP endpoint, with streaming progress updates and verified git diffs returned to the editor.

Server that enhances the capabilities of the Cline coding agent. It provides intelligent code suggestions, reduces hallucinations, and documents the knowledge base by leveraging your project's documentation and detecting the technologies used in your codebase.

A production-oriented MCP server for coding agents that enables multi-project management through secure file operations, Git integration, and safe command execution. It supports project discovery across multiple root directories and provides robust audit logging with both STDIO and HTTP transport options.

Enables AI agents to orchestrate tasks as a DAG with automated validation loops, executing validation commands, tracking statuses, and allowing iterative code fixes until tasks pass.

Enables any MCP client to read, search, patch, and execute commands in a codebase, including interactive sessions and git operations, with permission modes and safety boundaries.

Provides coding agent capabilities including file operations, terminal commands, search functionality, and utility operations.

An MCP execution layer that lets capable AI models safely control a developer workstation for file edits, terminal and process execution, Git operations, language-server analysis, browser and Windows UI automation, media processing, job/resource control, and checkpointing. It also coordinates durable single- or multi-agent tasks across providers with leases, dependency graphs, evidence logs, repair loops, and separate verification gates.

Enables delegating coding tasks to multiple AI CLI agents (Claude Code, Antigravity, Codex, Cursor) with session memory and diff inspection.

Specialized MCP server for coding workflows with web search and image analysis tools.

A transport-agnostic MCP server that integrates multiple AI coding agents (Claude Code, Gemini, and Codex) with built-in tools for command execution, calculations, and streaming capabilities. Supports both STDIO and HTTP transports for flexible deployment.

Enables a cloud coding agent to delegate a focused coding task to a local Ollama or OpenAI-compatible model through a bounded workspace, file allowlist, and required external checks. Changes are applied only after verification against the declared scope, with failed post-apply checks rolled back and the rollback itself confirmed.

Enables AI agents to develop within a local project workspace by reading and modifying files, running commands and tests, checking Git state, and persisting progress as history sessions that can be restored in later conversations.

A Rust + Tauri 2 desktop application that turns local project folders into persistent MCP workspaces, enabling AI agents to read files, modify code, run commands and tests, check Git status, and maintain cross-conversation development context through historical checkpoints, with built-in WSS/FRP/Cloudflare tunnels and OAuth for connecting to ChatGPT.

Empower any MCP-compatible AI Agent(MCP Client) with engineering-grade capabilities to understand, modify, run, and deliver real-world code repositories.

MCP coding agent with read-only tools and one paid tool for Paybond sandbox integration, enabling quick smoke testing and agent operations.

Enables autonomous creation, orchestration, and validation of production software, specialist coding agents, and complete multi-agent systems through Model Context Protocol.

A personal AI coding assistant that connects to various development environments and helps automate tasks, provide codebase insights, and improve coding decisions by leveraging the Model Context Protocol.

Transforms any prompt into a fully functional, production-ready product with zero human intervention by providing 150+ autonomous tools covering all aspects of software development.

Enables AI coding agents to read and write a user's Figma file through the Figma Plugin API, offline and privately, without API tokens or rate limits.

A local MCP server that lets Hermes supervise Claude Code, delegating focused coding, research, or review tasks to the Claude Code CLI and managing worker sessions, background jobs, cancellations, and read-only reviews.

A graph-based MCP server that provides AI coding agents with persistent memory to store patterns, track complex relationships, and retrieve knowledge across sessions. It leverages graph structures to handle temporal queries and relational paths that traditional vector stores often miss.

Lets a coding agent hand a plain-language app idea to a hosted AI software team that shapes, designs, builds, tests and deploys it, returning a one-time link into the full build workspace. Also lists your apps with their stage, pending approvals and live addresses after a standard OAuth sign-in.

Use Codex as a subagent inside Claude Code: async dispatch into a long-lived codex app-server, results pushed back into the Claude session automatically, steer/interrupt mid-task, and Codex's built-in code review.

Bridges Claude Code CLI with Cursor IDE to use your existing Claude subscription without separate API costs.

Enables ChatGPT to inspect selected local projects, make edits, run commands/tests, and review diffs through an authenticated MCP endpoint backed by a containerized workspace.

Enable Claude (or any other LLM) to interactively debug your code (set breakpoints and evaluate expressions in stack frame). It's language-agnostic, assuming debugger console support and valid launch.json for debugging in VSCode.

Containerized MCP server for coding agents with tools for file editing, shell commands, process management, Git, browser automation, and project snapshots.

An MCP server that provides tools and resources for managing a coding project's todo list, allowing users to view, add, update, and delete todo items with details like priority, status, and tags.

Enables Windows users to connect Antigravity CLI as an MCP subagent for coding tasks such as file reading, code execution, edits, and terminal commands, with model selection, conversation continuity, and streaming progress.

An MCP server that enables AI-powered IDEs to implement a structured development workflow from requirements gathering to code implementation, guiding users through goal collection, requirements specification, design documentation, task planning, and execution.

Rewrites coding prompts for optimal results with AI IDEs like Cursor by using Claude to add structure, context, and language-specific considerations to user prompts.

Production-grade, autonomous Model Context Protocol (MCP) server that elevates AI models from stateless code generators into persistent, self-verifying software engineers.