"Information about computer usage" matching MCP servers:

An MCP server giving coding agents context-window-aware code search and safe, atomic multi-file edits — built to cut token usage on large codebases without sacrificing correctness.

Enables coding agents to search and retrieve persistent repository memory created by Cliper, providing context, gaps, architecture, and timeline information via MCP tools.

Enables delegating local background tasks through an existing Codex or Claude Code subscription login and retrieving their status, answers, and usage.

MCP server for Python build123d to help AIs develop and reason about 3D models and CAD

Enables coding harnesses such as Claude Code, opencode, and Codex to hand tasks off to Perch AI as an autonomous worker and receive structured results with thread ids, model identity, and token usage. Supports asynchronous job tracking, thread continuation, and cancellation of long-running delegations.

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

Cloud Linux desktops for AI agents. Create a computer, see its screen through screenshots, and drive it with click, type, drag and keys, or hand off a whole task with run_agent. Also hosted with OAuth at https://app.mandala.computer/mcp.

Wraps the cursor-agent CLI to provide cost-effective tools for repository analysis, code search, planning, and editing. Offloads heavy thinking tasks from the host AI to reduce token usage while maintaining precise, scoped workspace operations.

Enables coding agents to navigate and query source code by providing context, symbols, and call graph information through a graph index.

A local MCP bridge that lets ChatGPT control opencode sessions for code modification, file reading, and repository management on your own computer.

A local project intelligence, batch coding, and self-learning engine for Codex that enables task-level batch coding, reduces token usage, and automatically learns skills.

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 users to run shell commands with compressed output, extract information from large files, drive local opencode and Codex agents, perform browser-automation tasks, and monitor runs via a live dashboard.

Enables MCP-compatible coding agents to run the local Antigravity CLI as a coding agent, manage conversation context and common options, and inspect usage, quota, models, version, help, and read-only slash commands.

Enables ChatGPT conversations to directly inspect local workspaces and delegate coding tasks to a local Codex agent, with controlled concurrency, reusable sessions, and task/usage tracking.

A local MCP bridge that lets ChatGPT chat mode operate the user's own computer, running project, file, and shell tasks through a local executor and returning progress and results to the conversation. It also coordinates Codex sessions (persistent or one-off) and other agent CLIs as sub-agents.

Enables Claude Code, Codex, and Cursor to delegate self-contained coding tasks to one another on the user's own subscriptions, with each task isolated in a git worktree and sandbox, usage tracked, budgets enforced, and results returned for review.

Enables external MCP clients to drive DeepSeek Harness agents for real coding tasks, providing tools for task execution and queueing, session management, sandboxed file access, preset switching, and usage statistics.

Implements Agentic Context Engineering to create self-improving AI coding assistants that learn from execution feedback and build persistent knowledge playbooks. Reduces token usage by 86.9% while improving code accuracy by 10.6% through incremental context updates.

Wraps the Codex engine in a local stdio MCP server so any MCP-capable agent can run coding turns as tools, with blocking runs, start/poll progress, command approvals, interrupts, thread resume, and token usage. It also supports custom OpenAI-compatible providers, letting teams blocked by signup, payment, or network limits still use the engine.