Packages

Documentation MCP server enabling AI agents to serve and search technical documentation — indexing documentation sites, Markdown files, or API references, and exposing them as searchable knowledge bases for agents. Makes local or hosted documentation accessible to AI agents for context-aware responses without requiring documentation to be in the LLM's training data.

MCP Forge scaffolding tool and template system for creating new MCP servers — providing project templates, code generation, and bootstrapping utilities to quickly create well-structured MCP servers with Go, reducing boilerplate and helping developers follow MCP protocol best practices from the start.

MCP server that transforms any OpenAPI specification into MCP tools, enabling AI agents to discover and call REST API endpoints dynamically. Supports multiple tool loading modes, mTLS, and custom auth headers.

An MCP server (also available as a Claude Desktop Extension) that enables AI agents to search Airbnb listings by location, dates, guests, and price range, and retrieve detailed property information including amenities, house rules, and coordinates.

MCP server that enables LLMs to control iOS simulators through natural language commands via Facebook's idb. Supports simulator management, app lifecycle, UI interaction, screenshot capture, accessibility inspection, location simulation, crash log monitoring, and screen recording.

Microsoft Office MCP server enabling AI agents to create and manipulate Microsoft Office documents — generating PowerPoint presentations, creating and editing Word documents, working with Excel spreadsheets, and integrating Microsoft Office document creation into agent-driven content and productivity workflows.

Node.js MCP server template or base implementation providing a foundation for building custom MCP servers in Node.js/TypeScript — including project structure, example tool implementations, TypeScript types, and development tooling setup. Serves as a starting point for developers building their own MCP servers with the JavaScript/TypeScript ecosystem.

Ollama MCP Bridge enabling AI agents to use Ollama local LLMs as MCP tools — routing tool calls and LLM inference requests to locally-running Ollama models, enabling agents to delegate tasks to local models (Llama 3, Mistral, Phi, Gemma, etc.), and providing a bridge between MCP-based agent frameworks and Ollama's local inference API.

Official OpsLevel MCP server enabling AI agents to interact with OpsLevel's service catalog and internal developer platform — querying service metadata, checking maturity scorecards, managing ownership, and accessing service health and compliance data.

Community MCP server for PostgreSQL enabling AI agents to execute queries, inspect schema, and manage data in PostgreSQL databases — the foundational relational database for agent data access.

Managed Redis service providing in-memory data storage for caching, session management, pub/sub messaging, and vector similarity search with Redis Stack modules.

Official MCP server from ScoutAPM (scoutapp org) for Scout APM — the developer-friendly application performance monitoring service. Enables AI agents to query Scout APM for transaction traces, performance bottlenecks, slow queries, memory/CPU metrics, and deployment tracking — supporting AI-driven performance analysis workflows.

Terminal MCP server enabling AI agents to execute shell commands and interact with the local terminal — running bash/shell commands, capturing stdout/stderr, managing working directories, and integrating system-level command execution into agent-driven development and automation workflows.

Efficiently consolidates project files into a single formatted output for LLM context, with intelligent filtering of binary/build artifacts, .contextfiles configuration, encoding support, and both CLI and MCP server interfaces.

Datadog MCP server enabling AI agents to interact with Datadog's observability platform — querying metrics and dashboards, searching logs, checking monitor status, retrieving APM traces, and integrating Datadog's monitoring capabilities into agent-driven incident response, SRE automation, and observability workflows.

KSail MCP server enabling AI agents to manage local Kubernetes clusters — creating and managing Kind/K3d clusters, deploying workloads, managing Kubernetes manifests, and integrating local Kubernetes development workflows into agent-driven DevOps and platform engineering automation.

MCP server for Mistral AI — the European open-weight and commercial LLM provider. Enables AI agents to call Mistral models (Mistral Large, Mistral Small, Codestral, Mixtral) for text generation, code completion, and embeddings. Useful for European data-residency requirements where US-based AI providers may be restricted.

MCP server providing AI agents with direct pandas DataFrame manipulation capabilities — loading CSV/Excel/JSON files into DataFrames, performing aggregations, filtering, joining, pivoting, and statistical analysis through MCP tool calls. Brings Python's most powerful data manipulation library into agent workflows without requiring code generation.