"Performing Email-Related Operations in Python" matching MCP servers:

A multi-agent operations assistant that answers business questions by searching local documents and order records.

Enables Codex to rank available tools and skills, rerank code and documentation search results, and triage saved output artifacts using Jev-based relevance scoring while leaving final decisions to Codex.

A Python MCP server providing AI agents with tools for local knowledge base search, business context retrieval, and text summarization prompt generation.

A Model Context Protocol server that enables AI agents to query a Graphiti knowledge graph and pgvector document store for evidence-backed responses via hybrid search and RAG.

All In One Model Context Protocol: qdrant, google suite (gmail, calendar, ...), jira, gitlab, CLI, ...

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

Processes emails from Outlook with date filtering, storing them in SQLite databases while generating vector embeddings for semantic search capabilities in MongoDB.

An MCP server exposing internal business operations as tools — task management (create, list, update status) and RAG-style semantic search over an internal knowledge base (leave, expense, and onboarding policies) that any MCP-compatible AI agent can call directly for grounded, non-hallucinated answers.

Enables agents to optimize context-window attention by reordering retrieved documents so critical evidence lands at prompt boundaries, mitigating lost-in-the-middle degradation. It combines BM25 lexical scoring, dense vector similarity, reciprocal rank fusion, knowledge-graph expansion, and recency-decay memory caching, exposed as a zero-dependency MCP stdio server.

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.

Provides a retrieval/MCP knowledge server for the Unreal Engine Python API, enabling AI agents to retrieve real signatures and documentation before writing code.

A Python implementation of the Model Context Protocol that enables applications to provide standardized context for LLMs, allowing developers to build servers that expose data and functionality to LLM applications.

Enables cloud and platform engineers to investigate AWS incidents by grounding responses in approved runbooks and operational evidence, with authenticated access, citations, observability, and human approval before any write action.

A Python port of the official Perplexity MCP server that enables web-grounded Q\&A, deep research, and step-by-step reasoning via Perplexity AI's models. It supports both STDIO and HTTP interfaces for seamless integration with tools like Claude Desktop and Cursor.

A Node In Layers package that simplifies creation of MCP (Model-Control-Protocol) servers with tools for defining models, adding CRUD operations, and interacting with clients.

A Python-based server providing persistent memory management for AI models with SQLite and Markdown dual backend storage. It features full-text search, RAG-enhanced querying, and cross-project knowledge sharing for integration with Claude, Cursor, and Rovo Dev.

An MCP server that analyzes Python codebases using AST, stores code elements in a vector database, and enables natural language queries about code structure and functionality using RAG with Google's Gemini models.

Rust-powered PDF toolkit over MCP: create, read, and analyze PDFs; extract text and entities for RAG; convert to Markdown; split/merge/rotate/reorder pages; manage form fields and annotations; encrypt documents. Runs locally via uvx oxidize-mcp.

An MCP server that serves documentation and enables AI-powered search, Q\&A, and document analysis for developer tools and guides.

Enables retrieval and cleaning of official documentation content for popular AI/Python libraries (uv, langchain, openai, llama-index) through web scraping and LLM-powered content extraction. Uses Serper API for search and Groq API to clean HTML into readable text with source attribution.