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Workspace Qdrant MCP

by ChrisGVE

Project-scoped code and documentation memory with hybrid search, exact grep, persistent rules, and Qdrant storage.

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Install Workspace Qdrant MCP

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Overview

ChrisGVE/workspace-qdrant-mcp MCP server indexes project code and documentation in Qdrant for project-scoped retrieval by AI coding assistants. It combines semantic and keyword search, uses tree-sitter chunking and LSP integration for code intelligence, and monitors registered projects through a background daemon. Reach for it when an assistant needs searchable workspace context, exact text matching, saved notes, or persistent behavioral rules across sessions. The server is in alpha and is being rebuilt for a planned v0.2.0 release.

Use cases

  • Search a registered codebase using semantic or exact matching
  • Store design decisions and notes for later sessions
  • Retrieve project documents by ID or metadata
  • Manage persistent behavioral rules for coding assistants
  • Browse project files and folder structure

Key features

  • Hybrid dense and sparse search with Reciprocal Rank Fusion
  • Tree-sitter semantic chunking and LSP integration
  • Qdrant-backed project-scoped storage
  • FTS5 substring and regular-expression search
  • Automatic project detection and background file monitoring
  • Persistent rules, notes, and project registration

Tools6

What Workspace Qdrant MCP lets your agent do. Expand a tool for its parameters.

Self-reported~73 tokens of context

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 2d ago

Recent health check succeeded.

13/13 checks healthy over the last 78d

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars4Stargazers on the source repository.
Last commit1mo agoMost recent push to the default branch.
Tools exposed6Callable tools this server registers over MCP.
Directory activity2 viewsConfig copies, upvotes, and views on AllMCPs.

How Workspace Qdrant MCP works

What ChrisGVE/workspace-qdrant-mcp MCP server does

ChrisGVE/workspace-qdrant-mcp MCP server provides project-scoped memory for AI coding assistants. It stores code, documentation, notes, and behavioral rules so an assistant can recover context across sessions instead of repeatedly inspecting a repository from scratch. Indexed content is kept in Qdrant, while project awareness is based on Git repository detection and registered project scopes.

The server separates project code and documentation from reference libraries, scratchpad material, and persistent rules. The supplied setup guidance describes using project collections for codebases, libraries for external documentation, scratchpad entries for reasoning and design decisions, and rules for user-defined behavioral instructions.

How it works

A background process watches registered project files and processes changes for ingestion. Code is divided with tree-sitter semantic chunking, with LSP integration used for active projects. Search combines dense semantic similarity with keyword matching through Reciprocal Rank Fusion, while exact substring and regular-expression queries use FTS5.

The MCP interface runs as a local stdio command named workspace-qdrant-mcp. Qdrant must be available separately; the example configuration points the server at http://localhost:6333. The project also provides the Rust-based wqm command-line tool for status and health checks, plus the memexd daemon for ongoing file monitoring and processing.

Setup and configuration

ChrisGVE/workspace-qdrant-mcp MCP server requires a running Qdrant instance. A C compiler is needed the first time Tree-sitter grammars are compiled. Clang and LLVM are additionally needed when building memexd from source with its default graph backend; prebuilt binaries do not require that toolchain.

Installation options include a Homebrew package for macOS and Linux, downloadable binaries for macOS, Linux, and Windows, or a source build using the repository’s installation script. The MCP configuration passes QDRANT_URL in the server environment. After installation, wqm --version and wqm status health provide basic verification.

For Claude Desktop, it provides a claude_desktop_config.json entry using the workspace-qdrant-mcp command. Projects should be registered before relying on automatic watching and ingestion.

Tools and capabilities

The six listed MCP tools are:

  • search combines semantic and keyword retrieval across indexed content.
  • retrieve fetches a document by identifier or metadata filter.
  • rules lists and manages persistent behavioral rules.
  • store saves content, registers projects, and records notes.
  • grep searches indexed content with exact substrings or regular expressions through FTS5.
  • list shows project files and folder structure.

Limitations and notes

ChrisGVE/workspace-qdrant-mcp MCP server is marked alpha. Treat the current implementation as active development software and consult the repository documentation for parameters and platform-specific installation details. The server depends on Qdrant and, for some source-build paths, local C or C++ compilation tools.

Read the full README View source on GitHub

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Frequently Asked Questions about Workspace Qdrant MCP

ChrisGVE/workspace-qdrant-mcp is an MCP server that connects AI coding assistants to Qdrant-backed project memory. Its main tools search indexed content, retrieve documents, store notes or projects, run exact grep queries, list files, and manage persistent behavioral rules across registered workspaces.

Install it with Homebrew, a platform-specific prebuilt binary, or the repository’s source installation script. Start Qdrant first, then configure the MCP command and `QDRANT_URL`.

Set `QDRANT_URL` to the address of a running Qdrant instance. The example uses Docker to publish Qdrant at `http://localhost:6333`, then passes that URL in the MCP server environment configuration.

It supports hybrid semantic and keyword search through `search`, plus exact substring and regular-expression matching through `grep`. Hybrid results use Reciprocal Rank Fusion, while exact matching is implemented with FTS5.

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