MCP Servers
The open registry for Model Context Protocol servers. Find the right tools, resources, and prompts for your AI agents — filtered by category, transport, or use case.
Servers
206
Tools
545
Categories
11
Contributors
182
Fivetran's MCP server helps agents inspect data connectors and sync health, diagnose broken connections, and answer operational questions about managed ELT pipelines.
Google's official MCP server for the Google Ads API, exposing resources and tools that let agents analyze advertising accounts and support campaign operations.
Google's official MCP server for Google Analytics 4. Lets AI agents run reports against the GA4 Data API, discover dimensions and metrics, and explore account and property metadata with server-side aggregation and safe defaults. Runs locally (via pipx) and connects to MCP-compatible clients for conversational analysis of website traffic and user behavior.
A Model Context Protocol server for Google Drive that lets AI assistants list, search, and read files stored in Drive, with automatic export of Google Docs, Sheets, Slides, and Drawings to readable formats. Authenticates via OAuth 2.0 and exposes Drive files as MCP resources so agents can ground their answers in your documents without copying data into chat first.
Google's official collection of Model Context Protocol servers that expose Google's security products to MCP clients. Provides access to Google SecOps (Chronicle) for SIEM search and case management, Google Threat Intelligence for indicator and malware lookups, and Security Command Center for cloud posture and findings. Lets security agents investigate alerts, enrich indicators of compromise, and triage vulnerabilities directly from natural language.
A Model Context Protocol server for Grafana Tempo that lets AI assistants query and analyze distributed tracing data. Agents can search traces with TraceQL, fetch a trace by ID, and inspect spans to investigate latency and errors across services during incident analysis.
Groq's official MCP server, giving agents access to Groq's high-speed inference stack. Supports fast chat and reasoning completions, vision models for interpreting images, text-to-speech and speech-to-text, batch processing for high-volume workloads, and lookups against Groq's own documentation.
GrowthBook's official MCP server for working with feature flags, experiments, and metrics so agents can safely support product experimentation workflows.
Harness' official MCP server for connecting agents to software delivery workflows, including deployment pipelines, services, environments, and delivery insights.
HashiCorp's official MCP server for Consul, providing integration with the Consul API for service discovery, configuration management, and service mesh operations. Lets AI agents list registered services and their health, read and write KV configuration entries, and inspect mesh intentions and config entries.
A Model Context Protocol server for HashiCorp Nomad that lets AI agents inspect and operate a Nomad cluster. Supports listing jobs, allocations, and nodes, reading allocation logs, and submitting or stopping jobs, which helps with debugging scheduling issues and running day-to-day workload operations.
Honeycomb's MCP server that lets AI assistants query and analyze observability data, including events, traces, alerts (triggers), and boards. Agents can run queries against datasets, inspect columns and schemas, and cross-reference production behavior with the codebase to investigate incidents. Connects to Honeycomb via API key or OAuth.
Infisical's official MCP server for its open-source secret management platform. Lets AI agents list projects and environments and create, read, update, and delete secrets and folders through controlled tools, so assistants can help manage application configuration without exposing raw credentials in code. Authenticates using a machine identity.
InfluxData's MCP server connects agents to InfluxDB 3 for SQL queries, schema exploration, and time-series database operations.
JetBrains' official MCP server for working with IntelliJ-based IDEs and Android Studio, enabling agents to inspect projects and use IDE-aware development capabilities.
JFrog's MCP server lets agents work with Platform services for artifact repositories, build information, release lifecycle management, and software supply-chain workflows.
An MCP server for Keycloak administration, enabling AI-assisted management of users and realms. Lets agents create, look up, and disable users, assign roles and groups, and inspect realm configuration through the Keycloak Admin API, which is useful for identity operations and onboarding or offboarding workflows.
LlamaIndex's official MCP server for LlamaCloud, letting agents query managed retrieval indexes and extract structured data from documents. Each configured index is exposed as its own tool, so an assistant can run semantic search over a specific knowledge base and pull parsed fields out of PDFs and other files.
Mapbox's official Model Context Protocol server, giving AI agents access to Mapbox's geospatial APIs. Supports forward and reverse geocoding, routing and directions, isochrone and matrix travel-time computation, and static map image generation, so assistants can answer location questions and build map-driven workflows from natural language.
Medusa's MCP integration connects agents to commerce data and administrative operations for storefront, catalog, order, and customer workflows.
Official MCP server for Meilisearch, the open-source, lightning-fast search engine. Enables MCP-compatible clients to manage search indexes, add and update documents, run searches, and adjust index settings through natural language. Useful for building and debugging search experiences and for letting agents query application data stored in Meilisearch.
Skills vs MCP servers
what’s the difference?Skillsthe “what to do”
A skill packages know-how — instructions, an example prompt, and recommended models — so an agent performs a task consistently. Skills add knowledge, not new connections.
MCP serversthe “how to connect”
An MCP server gives an agent new capabilities by connecting it to real systems (databases, APIs, files) over a transport. MCP adds connections and actions, not task instructions.
Rule of thumb: reach for a skill when you need the model to do a task well, and an MCP server when you need it to reach a tool or system. They compose — a skill can rely on tools an MCP server provides.
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