"Slack Bot with RAG Document Retrieval and Automated Response System" matching MCP servers:

Enables AI agents to create, validate, and export UML system models using structured MSON format. Supports generating PlantUML and Mermaid diagrams with direct export to System Designer macOS application.

A sophisticated server that enables AI assistants to automatically analyze codebases and generate comprehensive, professional documentation.

Enables AI coding agents to navigate and understand entire codebases through symbol-aware semantic retrieval and relational call graph traversal, supporting structural impact analysis and code modification workflows over MCP.

Enables semantic search and call-graph exploration over decompiled Unity Mono C# codebases, with hybrid retrieval, LLM re-ranking, and lazy-loaded project indexes.

Provides structured instructions to Cursor for generating comprehensive system summaries of Node.js/TypeScript codebases.

A local-first MCP server that indexes code repositories and enables AI assistants to search, fetch, and navigate code using BM25 hybrid retrieval, supporting common languages and tools like Codex and ChatGPT.

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.

A fully local RAG MCP server for semantic code search and code intelligence, using AST-level chunking and hybrid search to pinpoint functions, classes, and APIs. No cloud, no API keys, zero setup.

Read-only MCP tools for coding agents to audit deployment targets, detect fabricated code, review backlog, database schema, analytics, ML models, architecture docs, and decision lenses.

MCP server for searching, browsing, and analyzing decompiled Minecraft source code locally. Supports symbol lookup, text search, reference lookup, and lightweight RAG.

Enables token-efficient semantic search and analysis over any directory of files through hybrid search, directory overview, structural analysis, and dependency graphs.

Local repo-intelligence MCP for coding agents: indexes source, symbols, call graphs, git/GitHub history, and source-bound repo memories into local database.

Combines a knowledge graph with RAG (Retrieval-Augmented Generation) capabilities for semantic code indexing and search. Enables creating entity relationships, managing observations, and performing semantic searches across indexed codebases.

An MCP server that ingests Spring Boot REST microservices from GitHub, builds a request-flow ontology, and enables natural-language queries about code with flow-aware answers and citations.

Provides a real API for coding agents to look up design system components, props, and tokens, preventing guessed answers.

Enables coding agents to search local repositories through hybrid keyword and embedding retrieval, read matched chunks, and list indexed repos via MCP, without API keys or cloud services.

Enables AI assistants to perform hybrid semantic and lexical code search across multiple repositories, retrieve symbol definitions and call hierarchies, and manage repository relations through MCP tools.

Enables AI agents to chat with codebases by indexing local directories or GitHub repositories into a vector database for semantic search and code retrieval.

Enables LLM agents to efficiently understand and navigate a codebase by providing semantic search over symbols and a reference graph, replacing expensive grep/glob calls with structured tools like definition lookup, caller/callee queries, and change-impact analysis.

Fetches, summarizes, and reviews GitHub code diffs with asynchronous processing for large diffs.