#indexing

ContextBridge (CB) is a local-first retrieval layer that sits between your codebase and your AI coding agent. Instead of pasting raw source files into context or letting the AI guess which files matter, CB indexes your codebase's real structure (via Graphify) and returns a compact, ranked result — owner file, related files, key symbols, and dependency chains — grounded in your actual code. A typical response is ~4–8 KB instead of the 100+ KB of raw source an AI would otherwise need to read to answer the same question — roughly a 96% cut in input tokens sent to your cloud AI, with zero hallucinated file paths or method names.

Local-first code retrieval for AI agents — cuts codebase context from tens of thousands of tokens to a few hundred, with zero hallucinated file paths.

Chronicle MCP is a high-performance, universal local chat history archive connector designed specifically for Model Context Protocol (MCP) clients like Cursor, Claude Code, and daily development environments. Built natively in Python, it serves as an intelligent local storage and indexing layer that bridges your historical workspace transcripts directly into your active LLM context window. The tool addresses a critical bottleneck in AI-assisted engineering: context window bloat. By locally cleaning, formatting, caching, and dynamically chunking imported historical data, Chronicle MCP reduces context token usage by up to 40% while keeping your assistant fully informed of past decisions, codebase iterations, and project parameters.

Save tokens while coding — your AI agent gets structured code context, not file dumps. CodeWeave is an MCP server that gives AI agents structured code intelligence instead of dumping entire files into context. Your agent queries local indexes — AST, call graph, type graph, and hybrid semantic search — and gets back only what it needs. The result: less token waste, more relevant context, better coding decisions. It supports 7 languages (Python, TypeScript, JavaScript, Go, Rust, Java, C#) with a 6-stage hybrid search pipeline combining vector embeddings, full-text search, and structural density scoring. Everything runs locally with zero external dependencies beyond Ollama for embeddings. Monorepo and git worktree support included. A file watcher keeps indexes current as code changes — no manual reindexing needed. ATTENTION: Run "npx @codeweave/mcp" in your project directory first. The setup wizard handles everything for you. At first, it may take some time. INFO: Exhaustively tested on large Java, C#, JavaScript, TypeScript, React codebases. Contributions and feedback are welcome.

AI visibility monitoring and ΑΕΟ/SEO optimization platform. Track how your brand appears in AI assistant responses (ChatGPT, Claude, Perplexity), monitor competitor visibility, generate ΑΕΟ/SEO optimized content, manage social media publishing, and analyze website performance across search engines and AI platforms. Audit and create strategy for your brand.

Fast semantic code search for AI agents — find symbols, references, and callers across any codebase. Pre-built index committed to git, instant queries via MCP

An MCP (Model Context Protocol) server for indexing and querying codebases using CocoIndex. Features semantic code search with natural language queries, incremental indexing for fast updates, multi-language support (Python, JavaScript/TypeScript, Rust, Go, Java, C/C++, C#, SQL, Shell), flexible embeddings via local SentenceTransformers or 100+ cloud providers via LiteLLM, and portable SQLite storage with no external database required.

MCP Server that gives AI coding assistants instant access to your entire codebase through a persistent Tree-sitter index. Instead of grep flooding your context with 2000+ tokens per search, AiDex returns precise identifier matches in ~50 tokens. Supports 11 languages: C#, TypeScript, JavaScript, Rust, Python, C, C++, Java, Go, PHP, Ruby. Features include method signatures, cross-project search, time-based filtering, session notes, and an interactive browser-based viewer.

A high-performance, pure Rust Model Context Protocol (MCP) server that provides persistent, semantic, and graph-based memory for AI agents. Unlike other memory solutions that require a complex stack (Python + Vector DB + Graph DB), this project is a single, self-contained executable. ✅ No External Database (SurrealDB is embedded) ✅ No Python Dependencies (Embedding models run via embedded ONNX runtime) ✅ No API Keys Required (All models run locally on CPU) ✅ Zero Setup (Just run one Docker container or binary) It combines: Vector Search (FastEmbed) for semantic similarity. Knowledge Graph (PetGraph) for entity relationships. Code Indexing for understanding your codebase. Hybrid Retrieval (Reciprocal Rank Fusion) for best results.

The TwelveLabs MCP Server provides seamless integration with the TwelveLabs platform. This server enables AI assistants and applications to interact with TwelveLabs powerful video analysis capabilities through a standardized MCP interface.

A library for indexing code repositories using Tree-sitter Stack Graphs, designed to be used in MCP servers and other tools.

CLI tool to index MCP Servers to mcprepository.com

A service that monitors an Obsidian vault for file changes (new, modified, or deleted Markdown files) and indexes each note’s metadata and content into an SQLite database, exposing it for e.g. an MCP-server

A multimodal mcp server