"Managing and Working with Files" matching MCP servers:

Enables Claude Code to delegate software-engineering tasks to OpenCode agents working in the same workspace.

An MCP server that enables Claude to orchestrate multiple autonomous Claude Code agents working in parallel across different projects, with tools to dispatch, monitor, and manage their progress.

Enables teams to share structured, git-synced context among AI coding agents working on the same repository, including living plans, task declarations, handoff briefs, file-provenance history, and conflict detection.

Enables structured, iterative reasoning for complex problem-solving with features like confidence tracking, revision mechanisms, and branching support. Provides flexible validation and multiple output formats for systematic analysis and decision-making tasks.

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

Local ledger for development ports: leases a block of ports per project slot (one per working copy), renders env files, and resolves service names to URLs. No daemon, no listener.

Enables AI agents to offload and recall working memory as indexed episodes with LLM-written digests, reducing token usage and preserving detail across sessions.

Enables Claude Code to spawn child sessions for parallel, asynchronous task execution, allowing users to continue working while tasks run in the background.

Reduces Claude's context window costs by automatically summarizing inactive files to their public interfaces using AST parsing, keeping only the full contents of the currently active file.

Enables Claude Code to delegate autonomous web browsing, deep research, and multi-tab management to a Comet browser session, with tools for asking questions, capturing screenshots, managing tabs, switching modes, and uploading files.

Enables background window capture and input automation without moving the cursor or stealing focus, working across Windows, Linux X11, and Wayland systems.

A Model Context Protocol server that provides AI agents with secure access to local filesystem operations, enabling reading, writing, and managing files through a standardized interface.

MCP server to give new instructions to agent while its working. It uses the get_feedback tool to collect your input from the feedback.md file in the workspace, which is sent back to the agent when you save.

Enables AI agents to learn from their sessions by inspecting history, proposing rules or skills, and automatically persisting them to configuration files.

Connects agents to SynthNet (synthnet.io), the independent commons for working AI agents: join with a self-sovereign keypair identity, post and cite field notes, claim and deliver bounties, and build persistent reputation.

Gracefully closes Cursor work sessions by syncing context files across multiple AI assistants, updating Agent OS files, and automatically committing changes to git with descriptive messages.

Lets AI agents launch scans, inspect results, manage campaigns and policies, and read alerts directly against your Kaminari Ad workspace via your API key, with 82 tools covering most of the API surface.

Provides local persistent working memory for AI agents, allowing them to load rules, preferences, and project state before tasks and save validated experiences after successful completion. Works with stdio MCP clients via a local JSON file.

A Model Context Protocol (MCP) server that enables AI assistants to perform comprehensive file operations including finding, reading, writing, editing, searching, moving, and copying files with security validations.

Monitor failing GitHub Actions workflows, auto-fix them with OpenCode, and retry the run — driven by a workflow_run webhook, coordinated through an MCP server.