"Creating the Best General-Purpose Agent" matching MCP servers:

Provides LLMs with full PostgreSQL database access, including tools for query execution, schema management, and data export. It also features a dedicated insights system for storing business memos and supports both local stdio and remote HTTP transport.

Provides Python and FastAPI coding best practices with searchable guidelines, code review capabilities, and actionable improvement suggestions with examples across general coding, performance, and code quality categories.

A Model Context Protocol (MCP) server for producing better Terraform through CLI analysis (tflint, checkov, trivy, kics, infracost), best-practice guidance from terraform-best-practices.com, cloud provider recommendations (Azure, AWS, GCP), and Terraform Registry resource and module guidance.

Enables an agent to submit a task brief and receive a short, validated list of skills from its full catalogue that are worth reading before starting.

Enables to perform mathematical calculations, retrieve weather information, and manage files through a standardized MCP interface, showcasing best practices for building MCP servers.

MCP server for managing i18n translation files — gives your AI agent full control over your app's translations without dumping entire locale files into context.

Provides a stable, side-effect-free MCP tool contract that enables AI workflows to be prototyped, demoed, tested, or dry-run without connecting to databases, APIs, files, or other real data sources. It enables swapping in a real data server later without changing the AI-facing interface.

Enables coding agents to publish HTML pages and small sites over MCP, returning a stable link and keeping every version, with access limited to specific people, an organization, or anyone with the link.

Skill librarian for coding agents: describe a task and get the few matching skills from a large local skill collection, then report whether they worked. Runs locally with Ollama and SQLite.

Enables AI agents to directly manipulate Jupyter Notebook cells and structure through a secure, well-defined API, overcoming Cursor's AI agent mode limitations.

۩ Plan your projects with THC! MLLM planning harness for Software Engineering, modifiable for other purposes, released as an MCP server. Bring your own API keys. Open-Source now, my dear! :D

General purpose toolbox for AI applications

Local stdio MCP server for Bitwarden (Public API + Secrets Manager). 50+ admin tools for members, groups, collections, policies, licensing, projects, and secrets. For Claude Desktop / Claude Code.

Enables MCP clients to read bounded workspace files, validate exact replacement plans for the VS Code extension, and check recent typing progress without modifying source or running shell commands.

An MCP server that manages your conversation context automatically, pruning dead branches and keeping an audit trail of every cut.

Exposes 11 agent-native QA tools over stdio so coding agents can discover a project, analyze risk, select the smallest relevant test set, generate and dry-run tests, triage failures, propose tiered heals, analyze flakes, generate quality reports, and evaluate release readiness. Planning and healing tools stay read-only or propose-only, while execution defaults to dry-run, keeping agent actions safe and evidence-backed.

A multi-agent collaboration layer for AI coding agents enabling real-time communication, code review, and task handoff across distributed development sessions.

Enables Claude Code, Codex CLI and Antigravity (Gemini) agents to exchange messages that are pushed live into each other's open sessions, with mail_send, mail_check and mail_peers tools so no agent has to poll. Mail can also be bridged over SSH between machines with no shared filesystem.

AGENTS.md + skills orchestration with CHP Profile A R0/adversary gates. @cubiczan/agent-conductor

CLI and MCP server for AI agent strategy and deployment. Read blueprints, business cases, implementation plans, and specs from your coding agent. 18 tools for listing, retrieving, downloading, and updating blueprints. Exports full implementation specs as Agent Skills directories.

Enables an AI agent to boot and control an Android emulator — tapping, typing, scrolling, inspecting screens, reading logs and installing APKs — and to record those interactions as replayable, CI-ready test flows. Actions are captured by element text or id rather than coordinates, so the saved tests survive layout changes and rerun without the agent.

Provides various utilities including timezone-aware time queries, basic calculator operations, multilingual greetings, code review prompts, and AI image generation through Hugging Face API.

A daily census of the AI agent tooling ecosystem on GitHub (MCP servers, agent frameworks, skills): look up whether a repository is still maintained and under what licence, search by words with status and licence filters, get the day's totals and two acted-on lists. Standard library only, public metadata only.

Enables an opt-in manager–worker workflow where a manager agent plans work, dispatches scoped tasks with acceptance criteria to independent worker sessions, tracks progress on a live monitor, audits scope, and verifies artifacts by fingerprint before integrating them. Durable SQLite-backed state, heartbeats, and per-attempt logs support stop/resume, anti-duplicate dispatching, and recovery across closed chats.

Enables users to discover, redact, and upload coding-agent traces, then earn USDC/USDT by selling access to those traces to researchers and model trainers.

Provides a local markdown-based memory system for coding agents through MCP, enabling search, add, register, inventory, sync, and ingest operations across user and project memory. It gives any agent tool a durable, provider-agnostic shared memory stored in folders you own.

Exposes rate-limit and usage data for AI coding agent CLIs as MCP tools, enabling querying of usage snapshots and reset times.

Enables supervised, crash-resilient autonomous AI pair programming through nine MCP tools that checkpoint task progress in an atomic local DAG ledger, keep the machine awake during long runs, summarize repositories within a token budget, count tokens and costs across models, and rotate accounts before quota runs out. All state stays in a sandboxed project-local directory with zero telemetry, so long multi-step agent sessions survive crashes, context limits, and premature stopping.

A local-first bridge for moving useful context between coding agents, indexing sessions and creating handoff packets for seamless agent switching.

Enables AI agents to call deterministic finance tools (compound interest, loan payment, currency conversion, etc.) through MCP, with a shared tool registry used by both the agent loop and MCP clients.