Packages
4867 resultsMCP Server Azure DevOps
An MCP server that gives AI assistants full programmatic access to Azure DevOps — enabling natural language management of work items, repositories, branches, pull requests, pipelines, wikis, and code search across both Azure DevOps Services (cloud) and on-premises Server.
Ollama MCP Server
Ollama MCP server enabling AI agents to use locally-running language models via Ollama — sending prompts to local Llama, Mistral, Gemma, and other models, running privacy-preserving inference without cloud API costs, and integrating local LLM capabilities into agent-driven workflows requiring data privacy or offline operation.
ActivityWatch MCP Server
ActivityWatch MCP server enabling AI agents to query ActivityWatch — the popular open-source, privacy-respecting time tracking and activity monitoring tool. Enables reading time-use data (which apps were used when, browser history, editor activity), analyzing productivity patterns, querying time spent by category, and integrating personal productivity data into AI-assisted reflection and planning workflows.
Chrono Timezone MCP Server
MCP server for converting and comparing dates and times across any timezone with flexible, locale-aware formatting. Enables AI agents to handle timezone conversions, calculate time differences, schedule across timezones, and format datetime values for international workflows — solving the common timezone confusion in global team contexts.
Cocos Creator MCP Server
Official Cocos Creator MCP server enabling AI agents to interact with Cocos Creator game engine — managing scene nodes and components, querying game assets and resources, executing editor commands, and integrating Cocos Creator's game development environment into agent-driven game development automation workflows.
Dremio MCP
Official Dremio MCP server enabling AI agents to query and analyze data through Dremio's data lakehouse platform — executing SQL queries against data lakes (S3, ADLS, GCS), virtual datasets, and data catalogs using Dremio's semantic layer. Enables natural language to SQL conversion, data exploration, and analytics integration into agent-driven data workflows.
GDAL MCP Server
GDAL MCP server enabling AI agents to work with geospatial data using GDAL (Geospatial Data Abstraction Library) — reading and converting raster and vector formats (GeoTIFF, Shapefile, GeoJSON, KML, etc.), performing coordinate transformations, querying spatial data, extracting metadata, and integrating geospatial processing into agent workflows for mapping, GIS analysis, and spatial data pipelines.
Gemini Cloud Assist MCP Server (Google Cloud)
Official Google Cloud Platform MCP server for Gemini Cloud Assist, enabling AI agents to query GCP resources, get cloud assistance, manage infrastructure, and access Google Cloud services via natural language.
Ghost CMS MCP Server
Ghost CMS MCP server enabling AI agents to interact with Ghost publishing platform — reading and creating posts and pages, managing members and subscriptions, querying tags and authors, publishing content, and integrating Ghost's headless CMS capabilities into agent-driven content creation, editorial, and newsletter automation workflows.
Google Gemini API
Google's multimodal LLM API providing text, image, audio, video, and code understanding across Gemini 2.0 and 1.5 model families via AI Studio or Vertex AI.
JFrog MCP Server
Official JFrog MCP server enabling AI agents to interact with the JFrog Platform — managing artifacts in Artifactory, scanning packages for vulnerabilities with Xray, querying build information, and integrating JFrog's DevOps platform into agent-driven CI/CD and software supply chain security workflows.
Linkup MCP Server
Official Linkup MCP server providing real-time web search and content retrieval optimized for RAG (Retrieval Augmented Generation) workflows — giving agents access to current web information.
MCP Memory Server
MCP Memory server providing persistent memory and knowledge graph capabilities for AI agents — storing and retrieving facts, entities, and relationships across conversations, enabling agents to remember context between sessions, build up knowledge over time, and maintain long-term state without relying solely on in-context window memory.
MetaMCP
MetaMCP is a self-hosted MCP aggregator and gateway that groups multiple MCP servers into namespaces, exposes them as unified endpoints with configurable auth and rate limiting, and supports tool customization, middleware, and OpenAPI output in a single Docker deployment.
SymPy MCP Server
SymPy MCP server enabling AI agents to perform symbolic mathematics using Python's SymPy library — solving algebraic equations, computing derivatives and integrals, simplifying expressions, factoring polynomials, matrix operations, solving differential equations, and generating LaTeX output for mathematical expressions. Provides computer algebra system (CAS) capabilities to agents.
fetcher-mcp
MCP server that fetches and extracts web page content using Playwright headless browser, with Readability-based content extraction, JavaScript rendering, and batch URL fetching support.
Deno MCP Server (Official)
Official Deno MCP server enabling AI agents to interact with Deno runtime and Deno Deploy — executing TypeScript/JavaScript, managing deployments, querying Deno's built-in toolchain, and accessing the Deno standard library.
GitHub REST API
GitHub's comprehensive REST API for programmatic access to repositories, pull requests, issues, actions, code search, and all GitHub platform features.
Apple Books MCP
MCP server providing access to Apple Books library, highlights, and annotations. Enables AI agents to query your Apple Books reading library, access book metadata, retrieve highlights and notes made in books, and build AI-powered reading workflows — useful for knowledge management and second-brain systems.
DocFork Library Documentation MCP Server
Official MCP server from DocFork (docfork org) providing @latest documentation and code examples for 9,000+ libraries — similar to Context7 but from DocFork. Enables AI coding agents to fetch up-to-date library documentation, API references, and usage examples, ensuring agents use current rather than outdated training-data-era API signatures.