"Connecting Feishu (Lark)" matching MCP servers:

Provides access to Feishu (Lark) documents for AI-driven coding tools like Cursor, Windsurf, and Cline based on Model Context Protocol implementation.

Enables AI agents to use Feishu (Lark) spreadsheets as long-term memory and personal knowledge base with retrieval-augmented generation (RAG) via the Model Context Protocol.

askDB is an MCP server that retrieves relevant database schema (DDL) from a Pinecone index and provides it to LLMs to write SQL, without connecting to the database itself.

A fully modular RAG system with MCP protocol integration, enabling hybrid retrieval, multimodal support, and dual-mode transport for connecting to AI assistants like GitHub Copilot and Claude Desktop.

Provides semantic search capabilities by connecting Claude Desktop to a Cloudflare Workers backend powered by Vectorize. It enables natural language querying of knowledge bases using vector similarity and edge-based embedding generation.

Implements the Model Context Protocol (MCP) to provide AI models with a standardized interface for connecting to external data sources and tools like file systems, databases, or APIs.

A Model Context Protocol server that enhances LLM capabilities by connecting to Wikipedia, internet search (Tavily), and financial data (Yahoo Finance) tools, enabling contextual responses to user queries.

Enables connecting to CompanyGraph's mental-model as a remote MCP endpoint, allowing agents like Claude, ChatGPT, and Gemini to query the model's types, schemas, rules, and entities without authentication.

A Model Context Protocol server that enables Claude to perform Google Custom Search operations by connecting to Google's search API.

A federated bridge connecting any GraphQL endpoint to the Model Context Protocol (MCP) ecosystem, enabling LLMs to query and mutate graph data with zero-config integration.

Enables AI agents and MCP clients to search, retrieve, and read documents across unified knowledge sources including ShowDoc, GitHub, Gitee, ZenTao, Evernote, and Tencent Docs. It also supports connecting external MCP servers as additional knowledge sources.

Enables AI agents to search, read, and update persistent knowledge bases through 22 MCP tools, prompts, and resources, connecting to either a hosted endpoint or a self-hosted API.

an mcp for connecting notebooklm with any ai agent.

Enables AI agents to ask questions and review planning documents by connecting to OpenAI's GPT-4. Provides context-aware question answering and multi-level plan analysis with structured feedback including strengths, weaknesses, and suggestions.

A MCP server connecting to a managed index on LlamaCloud. This is a TypeScript-based MCP server that implements a connection to a managed index on LlamaCloud.

Indexes local and enterprise documents to provide a unified personal knowledge base for AI clients via the Model Context Protocol. It supports full-text search across various file formats and integrates with platforms like Feishu and WeChat Work.

Enables integration with financial transaction data through REST APIs, PostgreSQL databases, and document storage systems. Demonstrates agentic AI capabilities by connecting to Alpha Vantage API and managing financial data through natural language interactions.

Read-only MCP server connecting Codex to Neo4j for querying projects, clients, tasks, and SEO audit results.

Enables AI assistants to search a Zotero library, retrieve complete metadata for individual items, and read the full text of attached PDFs, connecting through either the desktop app's local API or the Zotero Web API.

Acts as a secure bridge connecting PostgreSQL databases to AI models, enabling natural language querying, schema analysis, and controlled write operations with multi-layer security.

Unified AI Agent SaaS Connector & Multi-Provider Gateway connecting to 1,000+ SaaS platforms, vector databases, and 500+ LLM models via MCP, REST API, and CLI.

Adds persistent memory to AI assistants by connecting to the Memphora cloud platform, allowing them to store and recall facts across conversations. It enables tools for searching memories, extracting insights, and maintaining long-term user context and preferences.

Local MCP server that turns Mark Builds Brands' YouTube knowledge into an AI coaching assistant for ecommerce and Facebook Ads.

An MCP server providing machine translation capabilities via the Lara Translate API, featuring language detection and context-aware translations between numerous language pairs.

Provides AI assistants with a persistent knowledge graph and experience memory, featuring context-aware routing, progressive disclosure, decay-based forgetting, and automatic promotion of important knowledge. It offers 22 MCP tools for storing, recalling, connecting, and querying knowledge, experiences, preferences, and ideas.

Enables AI agents to query version-aware, source-backed knowledge through MCP, using deterministic retrieval, durable research demands, and immutable validated revisions. It provides network and domain-specific guidance without connecting to devices or executing commands.

Enables MCP-compliant clients and autonomous agents to pick optimal few-shot in-context exemplars by combining semantic token overlap, cosine similarity, and Maximal Marginal Relevance diversity ranking for prompt optimization. Runs deterministically on the Python standard library with zero external dependencies, avoiding frontier LLM round-trips for sub-millisecond selection.

Enables agents to run hybrid retrieval, reciprocal rank fusion, knowledge graph expansion, semantic caching, and context reordering so critical evidence is placed at prompt boundaries to reduce lost-in-the-middle degradation.

Enables agents to ground claims in enterprise knowledge by hashing source chunks, tracing claims back to their origins, validating clearance boundaries, and maintaining audit trails to support anti-hallucination workflows.

Enables agents to retrieve relevant context via a zero-dependency hybrid search engine that blends sparse keyword-based lexical ranking with dense vector similarity scores into one fused result set, then refines it with knowledge graph triplet expansion and lost-in-the-middle context reordering. It also provides episodic memory consolidation, recency decay, and semantic query caching, exposed as a JSON-RPC 2.0 stdio MCP server for clients such as Claude Desktop, Cursor, and Windsurf.