"Information about Feishu (Lark) collaboration platform" matching MCP servers:

Enables running a local multi-agent pipeline that turns a product idea into a validated technical spec, with tools to generate, deterministically check, LLM-review, and retrieve stored runs.

Provides current weather information for any location and generates AI-powered responses using Google Gemini through FastMCP.

Demonstrates agent-native platform onboarding with human-in-the-loop API key provisioning, plus live usage and rate-limit queries via Anthropic Admin APIs.

Bridges Hermes Agent to the Hermes Intelligence Platform API, enabling tools to read/write learning loop data (context, feedback, signals, memory, etc.) via stdio.

A local MCP server that forwards tools from an upstream experiment platform and adds AI Agent tools for experiment analysis and skill debugging.

Enables MCP hosts like Cursor or Claude to query an enterprise knowledge base via stdio, providing RAG-based answers with cited sources.

About MCP server for Google Agent Platform Docs — Google AI + Vertex AI. 3400+ pages searchable by AI agents.

MCP server for Azure resource management, AI Foundry, and Entra ID — inspect and operate Azure infrastructure through AI agents.

Connects AI assistants to your Argo campaigns via the Model Context Protocol. Once configured, your AI assistant can read and write campaign lore, look up character details, and interact with Argo data directly from the chat interface.

MCP server giving Claude full control over ElevenLabs Conversational AI agents, conversations, knowledge base, tools, tests, telephony, and workspace settings.

Provides AI assistants with real-time web search, reasoning, and research capabilities through Perplexity's Agent API and Search API, enabling tools for search, conversational Q&A, deep research, and advanced reasoning.

Enables AI assistants to perform real-time web search, reasoning, and research through the Perplexity Agent API and Search API.

A standalone Python/FastAPI server that implements the Model Context Protocol (MCP) for the OPTIX threat intelligence platform. It exposes 26 analyst-friendly tools that AI assistants and programmatic consumers can use to query threat feeds, search documents and indicators, manage watchlists, triage IOCs, generate detection rules, trigger AI research, and produce intelligence reports.

Exposes multiple LLM providers (AWS Bedrock, OpenAI, Google Gemini, local Ollama) as MCP tools with automatic routing by task type and Prometheus metrics, enabling any MCP-compatible client to generate text, route prompts, and list providers.

An MCP-style stdio server for evaluating AI agent outputs, enabling CI-friendly quality gates, regression comparisons, and canary promotion decisions.

Enables product enrichment with sentiment analysis, category mapping, and attribute extraction; provides tools to fetch top products and cluster summaries.

Enables users to run synthetic support-agent evaluations, preserve and inspect traces, score results deterministically, inject failures, and enforce release review gates through MCP tools, resources, and prompts.

Automates GitHub Pull Request analysis using AI, integrating webhooks, OpenAI, and Feishu/Lark knowledge base for structured summaries.

Enables AI agents to manage personal expenses via natural language through add, query, summarize, and delete expense tools.

Enables portfolio risk assessment, sentiment analysis, and investment recommendations via MCP tools.