"Information about agents or agent-related topics" matching MCP servers:

Local, searchable project memory for AI coding agents. Markdown source of truth, MCP interface, safe structured updates

MCP server that keeps language server sessions warm and routes multiple languages through one process. Agents get persistent cross-file awareness, speculative execution (simulate edits before writing to disk), and 20 skills that encode correct multi-step operations like safe rename, blast-radius analysis, and end-to-end refactoring. Single Go binary, no runtime dependencies.

Universal session & handoff layer for AI coding agents: shared task, decision, blocker and test state, checkpoints, and deterministic handoff between Claude Code, Codex, OpenCode, Cursor, Cline, pi and omp. Local SQLite, git-aware, 25 tools + 7 resources over stdio or streamable-http.

Encoding-aware, indentation-smart file tools for AI coding agents. 20+ tools including read/edit with automatic encoding detection, smart indentation conversion, SSH, SFTP, process management, and system utilities.

Enables coding agents to coordinate in spaces through direct messages, topics, blackboards, and durable inboxes, exposing bus tools over MCP and RPC while pushing inbox updates over SSE.

Enables ChatGPT on Windows to securely access codebases, Git, terminals, language servers, debuggers, SQLite, local HTTP services, adaptive project memory, engineering skills, checkpoints, audit logs, and sandboxed command execution through MCP tools.

A local MCP server that lets AI agents send desktop notifications during tasks, ask for attention, and report completion. Supports macOS, Linux, and Windows, with optional webhook delivery.

Enables AI agents and MCP clients to drive real Chrome, Edge, Brave, and Firefox browsers through native automation protocols. It exposes tools for navigation, page snapshots, form filling, clicks, screenshots, console/network inspection, and tab management.

Provides git-aware safe file operations, allowing AI agents to delete files while protecting git-tracked source code from accidental deletion.

Exposes three read-only tools that list local Agent and MCP runtime candidates, inspect running processes, and check a live runtime against an approved artifact expectation. Returns explicit verdicts such as matched, stale, leaked, conflict, unknown, or not running without exposing argv, environment values, or file contents.

MCP Guard Server - Budget control, approval workflows and audit logging for AI agents (Claude Code, Cursor, ChatGPT)

A powerful MCP tool for forging and managing AI agent personalities with customizable expert traits and behaviors.

A memory, docs, and repo context layer for engineering agents, providing typed persistent memory, RAG document retrieval, and repository-aware tools to recall decisions, runbooks, and operational knowledge across sessions.

Enables agents to capture task-scoped residue evidence during test and build workflows, exposing files, directories, processes, and ports for user-authorized cleanup and post-cleanup verification without deleting or modifying anything itself.

Enables AI assistants to search PatchXNote records, inspect AI-generated results, create Markdown drafts, and send user-approved messages to webhooks while keeping server data read-only.

Provides persistent, searchable memory across AI coding agent and chat history (Claude Code, Codex, Gemini CLI, ChatGPT, and more) via retrieval-augmented generation, enabling semantic and hybrid search to retain context across sessions.

Trust layer for AI agents: check any x402 endpoint or MCP server before paying (live status, real price, payTo vs listing, OFAC), plus agent passports with two-sided signed receipts and signed skill releases. Every result is a signed event in a public transparency log.

Monitoring that agents set up for themselves — cron jobs, CI/CD pipelines and AI agent runs.

I gave Claude a mass grave of 200 markdown files and now it remembers my entire project between sessions. No cloud, no API keys, one 10mb Go binary, and private. Stateless Agent Memory Engine is your synapse to the CLI.

Enables users to interact with container registries through the ORAS CLI, providing information about container images, platforms, and signatures via natural language queries.

Local harness for Codex, Claude Code, opencode and other coding agents: agents record pitfalls and design decisions as Markdown files reviewed in the same PR as the code, and route only task-related records to the next agent.

Enables AI assistants to inspect uptime monitoring, incidents, status pages, alerts, dependencies, and reports. It also supports related changes such as opening incidents, posting updates, scheduling maintenance, adding monitors, and configuring alerts and reports.

Give your coding agent the dependency graph it is about to change: scan, diff, explain, audit.

Enables AI agents to publish notifications, wait for replies, and read message history through ntfy, with scoped agent tokens.

Enables AI agents to search and read IMAP messages across accounts and create new messages, replies, or forwards as drafts, without sending, moving, or deleting mail.

Enables governed multi-agent software factory operations through eight typed, audited tools for Sprint planning, submission, decisions, inspection, cancellation, and resumption.

MCP server that helps coding agents research and prepare GitHub contributions by gathering repository guidance, related work, code context, and validation evidence into a local SQLite corpus, without ever writing to GitHub.

Enables an agent to evaluate a target MCP server and verify attestations about it from inside the editor, using read-only, allowlisted tools that never send credentials.

Provides AI assistants with persistent graph database memory using Neo4j, enabling task management, relationship understanding, semantic search with embeddings, file indexing, and multi-agent coordination through the Model Context Protocol.

https://gopls-mcp.org/ MCP server for golang projects development: Expand AI Code Agent ability boundary to have a semantic understanding and determinisic information for golang projects.