"Extracting and Processing Real Estate Listings Using Vector Databases" matching MCP servers:

Enables semantic code search over a local codebase using Qdrant vector embeddings and OpenAI embeddings, allowing natural language queries from MCP-compatible clients like Claude Desktop.

A CLI-first semantic code search tool with MCP integration for AI assistants, enabling semantic search, AST-aware parsing, and code analysis across 13 languages.

Enables semantic code search across multi-language codebases using natural language queries, integrated with Qdrant vector database for fast, cached retrieval.

locidx

Enables processing documents larger than LLM context windows using symbolic commands in Nucleus DSL, executed by the Lattice engine, without chunking or vector databases.

MCP server with local vector search for your codebase. Smart indexing, semantic search, Git history — all offline.

coldstart gives coding agents durable memory of a specific codebase, with no embeddings, no vector store, and no API key. Agents write notes after real tasks — what a file is for, how a flow spans files, which invariants hold — and those notes are surfaced automatically to a later session whose task matches, so nothing gets rediscovered from scratch. Every note is anchored to real files by content

A local MCP server that provides semantic code search for Python codebases using tree-sitter for chunking and LanceDB for vector storage. It enables natural language queries to find relevant code snippets based on meaning rather than just text matching.

Enables efficient analysis of large codebases using Claude Code and Kimi K2.5 through the Model Context Protocol, with session caching and parallel processing to reduce costs and time.

Visual tech-stack inventory of any codebase: languages, frameworks, databases, AI SDKs, infra.

Provides semantic vector search over local codebases via MCP, enabling hybrid search (dense + sparse + RRF) for any MCP client like GitHub Copilot or Claude Desktop.

Provides Python refactoring capabilities via the Rope library, enabling AI agents to perform safe, project-wide code transformations such as renaming symbols, moving modules, and extracting methods.

Enables AI coding agents to reduce token usage by condensing source code into AST skeletons, extracting specific symbols, compressing logs, and estimating token budgets.

Provides intelligent semantic code search using local AI embeddings, enabling natural language queries to find relevant code by meaning rather than exact keywords. Indexes codebases in the background with smart project detection and privacy-first local processing.

A TypeScript/JavaScript refactoring MCP server that uses the TypeScript compiler to perform safe, type-aware code transformations such as renaming, extracting functions, and organizing imports across your codebase.

Enables mapping JavaScript error stack traces back to original source code, extracting context information to help developers locate and fix issues.

A toolkit for analyzing subreddits and post patterns to help choose communities and shape posts using real data, running as an MCP server and command-line tool.

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.

Routes narrow text-processing tools (classification, extraction, summarization, etc.) to budget models to reduce token consumption on the main model.

Converts vector PDF circuit schematics into evidence-preserving SchematicIR JSON with tools to inspect, convert, and validate schematic PDFs.