"How to organize or locate my project file" matching MCP servers:

A read-only MCP server that provides document awareness for agents by parsing local files into structured profiles, blocks, chunks, and search results, enabling agents to understand and cite document content without dealing with raw file formats.

Local workspace memory for Claude Desktop. Indexes documents into a vector store with hybrid search, cross-session memory, auto-learn, and knowledge graph.

A self-contained Claude Code plugin that profiles a project by running a fixed DAG to emit agent-facing context pages, a human/developer guide, and a queryable vectorstore.

This server provides an API to query Large Language Models using context from local files, supporting various models and file types for context-aware responses.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

A Python MCP server that gives LLMs persistent, searchable access to project context — documentation, architecture decisions, and session notes.

Enables searching and retrieving documentation from crawled documentation sites as an MCP server, allowing coding agents to query real docs instead of relying on training data.

Hardware-accelerated codebase mapping that indexes Git repositories into Postgres/pgvector and serves code search, relationships, and static analysis results via a stdio MCP server.

A tool that creates MCP (Model Context Protocol) servers from OpenAPI/Swagger specifications, enabling AI assistants to interact with your APIs.

MCP server that allows AI agents to read, search, summarize, and answer questions about local PDF, DOCX, XLSX, and TXT files through five tools.

Enables LLMs to query documents using semantic search, supporting PDFs, Word, Excel, and more. Organizes documents by topics from folder structure and provides advanced search features like phrase matching and date filtering.

Enables hybrid retrieval-augmented generation with semantic and keyword search over documents, supporting multiple formats and providing MCP integration for Claude Desktop.

Routes coding agents to the most relevant project documentation (decisions, intent, constraints) with provenance and freshness, providing tools for task routing, knowledge search, and document context.

Provides project-aware context for coding agents by selecting relevant AGENTS.md, docs, module paths, and decision records based on task, reducing redundant context loading via MCP stdio.

Enables Claude to perform hybrid search across local documents by combining semantic vector retrieval and BM25 keyword matching for optimal context recovery. It supports multiple file formats including PDF, CSV, and Markdown, leveraging local Ollama models for private and efficient document querying.

A customizable Model Context Protocol server built with mcp-framework that enables Claude to access external tools and capabilities through a standardized interface.

Automatically routes natural language questions to RAG or Text2SQL paths to answer queries about virtual construction site data, supporting semantic search and structured aggregation.

A Cloudflare Worker that transforms Cloudflare AI Search (AutoRAG) instances into an MCP server for querying documentation. It enables AI models to search and retrieve relevant information from custom document sets stored in R2 buckets.

Provides tools for searching and reading local files, supporting metadata filters, PDF full-text keyword search, and sandboxed file access.

Enables semantic search over personal files using natural language, with optional AI summarization, all running locally.