"Gmail automation tool or service" matching MCP servers:

Enables Claude Desktop to search private documents using Azure AI Search and perform web searches with Bing, providing AI-enhanced results with source citations through Azure AI Agent Service or direct Azure AI Search integration.

Enables LLM hosts to retrieve live, relevant documentation excerpts from official library docs sites via a search-and-RAG tool, avoiding reliance on training data.

Dual-mode tool providing long-term memory management for Claude Code and MCP clients, with semantic search and user/session isolation via Qdrant.

Enables natural-language semantic search of a project's code through a local vector index, so questions that plain grep cannot match return ranked file:line references. It also indexes or refreshes projects on demand, reports index status, and optionally flags near-identical code across files.

Provides AI agents with persistent, searchable memory using semantic search, auto-linking, and categorization, with zero-config local setup or production-ready external providers.

Enables agents to run semantic search across one or more local project directories by automatically maintaining a LAN-local Qdrant index with Ollama embeddings. Indexing, staleness checks, and incremental updates happen transparently, so users can query code by meaning without managing collections, chunks, or hashes.

Myelin provides persistent, brain-inspired memory for AI agents, enabling cross-session and cross-tool context retention with local, private storage.

An MCP server that retrieves resume/experience evidence relevant to a job description via vector RAG, and tracks fit-analysis results in a configurable tracking store (Notion or SQLite), with tools like match_job, push_to_tracker, and list_applications.

An MCP server for semantic search and retrieval of indexed Slack messages stored in Qdrant using Cohere reranking via AWS Bedrock. It enables users to search through Slack history, retrieve full message threads, and access channel or user statistics through natural language.

Turns a tools.yaml contract into typed, tenant-guarded vector database tools that agents can use via Python imports or MCP.

Lets AI assistants connect to Apache Solr deployments to enumerate collections and schemas, run full-text and filtered queries, apply faceting and sorting, and issue SQL or vector-semantic searches without building custom API glue. Communicates over stdio or SSE and requires minimal environment configuration instead of app-specific credentials or mandatory ZooKeeper wiring.

Local-first agent-memory MCP server with a why() tool: recall a fact together with its connected subgraph (multi-hop), so linked memories surface even when they share no words with the query. remember/recall/relate/forget/why over one fused vector + graph + columnar engine a single offline Rust binary.

MCP bridge to a multimodal RAG service, enabling hybrid search and Q&A over documents with tools for knowledge base queries and health checks.

A ChromaDB-based document storage MCP server that enables adding, storing, and summarizing notes with a custom note:// URI scheme and a single tool for note creation.

Exposes semantic document retrieval from a ChromaDB collection of arXiv papers as a callable tool for any MCP-compatible client.

Enables semantic search across Apple Mail, Messages, Calendar, and Contacts on macOS using natural language queries. All processing happens locally with privacy-first vector indexing for fast similarity search.

Enables Claude to interact with core AWS services like S3, EC2, RDS, and CloudWatch, along with a generic SDK wrapper for any AWS operation. It also supports cost monitoring and optional vector store capabilities for document ingestion and search.

It connects an agent to a PostgreSQL database so it can inspect schemas, run validated reads and writes, resolve fuzzy names via trigram search, and perform or store pgvector embedding searches. Vector and hybrid search operate through an OpenAI-compatible embeddings endpoint, letting agents query by text without handling embedding arrays.

A production-grade MCP server for integrating RAG into AI agents, supporting multiple vector databases with enterprise security and dynamic tool selection.

A flexible memory system for AI applications that supports multiple LLM providers and can be used either as an MCP server or as a direct library integration, enabling autonomous memory management without explicit commands.