"Development-related MCP resources" matching MCP servers:

将 pgvector 知识库检索能力封装为标准 MCP 服务器，支持 Tools、Resources 和 Prompts，使 Claude Desktop 等客户端可通过自然语言查询知识库。

Multi-modal RAG engine for AI assistants. Stores conversation history, conclusions, diffs, error traces, and other development artifacts in LanceDB with vector search, multi-factor scoring, and an LLM-driven consolidation pipeline.

Provides semantic search capabilities over the Plesk Extensions Guide documentation using Retrieval-Augmented Generation (RAG) and vector embeddings. It enables AI assistants to retrieve relevant technical information and answer natural language queries regarding Plesk extension development.

Enables AI development tools to perform semantic search and document relationship analysis on vectorized content stored in Qdrant databases.

A Model Context Protocol server integration that creates a persistent, searchable working memory for AI-assisted development by enabling automated context recall and knowledge persistence in Chroma, the open-source embedding database.

Context Portal (ConPort): A memory bank MCP server building a project-specific knowledge graph to supercharge AI assistants. Enables powerful Retrieval Augmented Generation (RAG) for context-aware development in your IDE.

Enables semantic search and interaction with Fuel Network documentation and Sway Language resources within AI-powered development environments. It provides high-quality technical context using a local vector database to assist with smart contract development and FuelVM queries.

Enables chat.z.ai agents to keep persistent vector memory, auto-enhance context, compress long content to save tokens, and orchestrate smart skills, with every session mirrored to the user's own private GitHub repository as a human-readable cloud database. Exposes these capabilities through a streamable HTTP MCP endpoint with 12 tools, resources, and prompt templates.

Enables users to ingest documents into a PostgreSQL/pgvector knowledge base, run semantic search over them, and get grounded answers through a retrieval-augmented generation pipeline backed by a free LLM. It also lets clients spin up specialized AI agents on demand and exposes knowledge-base stats and configuration as resources for tutoring workflows.

Enables MCP clients such as Cursor, Claude, and Windsurf to store and recall user preferences, behaviors, and other text-based information across conversations, using vector similarity search so conceptually related items are found even when exact keywords differ.

Gives Claude Code read-only semantic search over an Obsidian vault by reading the embedding files the Smart Connections plugin already produces, so no API key or network is needed at query time and it works across the WSL/Windows boundary. Exposes tools for meaning-based note and block search with folder scoping, related-note discovery, note reading, and index health checks.

Enables persistent memory and semantic search for development workflows with hierarchical compression. Store and retrieve development knowledge across IDE sessions using natural language queries, circumventing context window limitations.

Enables LLM-based agents and MCP-compatible clients to perform full CRUD operations on FHIR resources, plus AI-powered document ingestion, semantic search, and LOINC terminology lookup. Users can query and manipulate clinical data using natural-language prompts.

Enables LLM-based agents to perform full CRUD operations on FHIR resources, process clinical documents, and use semantic search through MCP-compatible clients using natural-language prompts.

Enables Claude to recall and query past conversations through a local knowledge graph and vector index, with tools for searching memory, retrieving related entities, and adding new memories.

Demonstrates an MCP server with tools, resources, and prompts for a knowledgebase chatbot that queries a ChromaDB vector database for RAG responses.

Enables LLM-based agents and MCP-compatible clients such as Claude Desktop to perform full CRUD operations on FHIR resources, manage documents with semantic vector search and a RAG-ready pipeline, and look up standardized LOINC terminology, all through natural-language prompts. It connects to FHIR-compliant backends like Medplum with OAuth2 authentication and supports Docker or uv deployment.

Enables LLM-based agents and MCP-compatible clients to query and manipulate clinical data through natural-language prompts by providing full CRUD operations on FHIR resources, document ingestion, semantic search, and LOINC terminology lookup.

Enables AI agents and MCP hosts to query locally indexed notes, docs, chat logs, and other files via hybrid vector/BM25 retrieval with section-level citations. Exposes tools, resources, and prompts for searching and asking over the indexed content.

Enables Claude to perform semantic search across your Obsidian vault using Smart Connections vector database. Provides meaning-based search, related note discovery, and context retrieval for RAG queries instead of basic keyword matching.