"Understanding Perl, Language Server Protocol (LSP), and Abstract Syntax Trees (AST)" matching MCP servers:

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.

A conversational application server that integrates LLM capabilities via Ollama with vector memory context, supporting multiple users, sessions, automatic history summarization, and a plugin system for executing real actions.

MCP server for semantic code search with AST-aware chunking, hybrid vectors, and query syntax.

A minimalist indexing tool that provides AI agents with semantic search and structural AST parsing for deep codebase understanding. It enables autonomous agents to navigate large codebases predictably using vector embeddings and native language server capabilities like definition and reference tracking.

Enables AI agents to interact with an embedded graph database (GrafeoDB) via the Model Context Protocol, providing tools for graph CRUD, GQL queries, full-text and vector search, and graph algorithms.

Provides semantic search over AWS Cloudscape Design System documentation using natural language queries, enabling AI assistants to efficiently find and retrieve component documentation with token-efficient responses.

Enables semantic search and analysis of customer support tickets. Provides tools to search tickets, analyze the dataset, and retrieve individual tickets using natural language.

Provides SQL-backed semantic search over indexed notes using pgvector, exposing tools to search and list note sources via natural language.

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.

Enables any MCP client to index files, directories, and arbitrary text into a local SQLite-backed vector database and perform semantic search with language-aware chunking, filters, and context expansion, all offline without network calls.

A Model Context Protocol (MCP) server that provides a local-first RAG engine for your markdown documents. It uses a file-based Milvus vector database to index your notes, enabling LLMs to perform semantic search and retrieve relevant content from your local files.

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.

Enables Claude to search a local hybrid retrieval index of research papers and ingest new PDFs, providing research-paper memory queryable directly through natural language.

MCP server for semantic code indexing using vector embeddings, enabling AI agents to maintain persistent memory of codebases through natural language queries and intelligent chunking.

A Model Context Protocol server for reading TiDB RAG knowledge base, enabling browsing, searching, and vector search of knowledge entries.

Enables interaction with Moorcheh AI services including namespace management, document embedding, vector search, and AI-powered answers through the Model Context Protocol.

Enables AI agents to search local Markdown documents using natural language, with automatic indexing and section-level retrieval.

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.

Indexes codebases using semantic embeddings for natural language search, enabling developers to find code with queries like 'how does authentication work'.

A production-grade semantic search server for food recipes — built for AI agents using the Model Context Protocol (MCP). Search across 50,000+ recipes with hybrid dense + sparse retrieval and cross-encoder reranking.

A server that enables vector and keyword search capabilities in Typesense databases through the Model Context Protocol, providing tools for collection management, document operations, and search functionality.

Model Context Protocol server for RosalindDB, enabling AI clients to create datasets, ingest vectors, run similarity queries, and check usage on a cost-optimized vector search database.

A Model Context Protocol server that allows Large Language Models to interact with Astra DB databases, providing tools for managing collections and records through natural language commands.

A Model Context Protocol server for Chroma, enabling AI models to create collections and retrieve data using vector search, full text search, and metadata filtering.

Enables AI agents to interact with Milvus vector databases and Zilliz Cloud through natural language, allowing users to create clusters, manage collections, insert vector data, and perform semantic searches directly from their AI assistants.

Model Context Protocol (MCP) server for TigerGraph that lets AI agents interact with TigerGraph through the MCP standard using pyTigerGraph's async APIs.

Cline MCP integration that allows users to save, search, and format memories with semantic understanding, providing tools to store and retrieve information using vector embeddings for meaning-based search.

A Model Context Protocol server providing vector database capabilities through Chroma, enabling semantic document search, metadata filtering, and document management with persistent storage.

Enables AI agents to manage and search data in Redis using natural language. Supports hashes, lists, sets, sorted sets, streams, JSON, and vector search.

A Model Context Protocol (MCP) server for advanced code analysis and editing with semantic search capabilities, enabling AI assistants to perform complex code operations through a standardized interface.