Workspace Qdrant MCP
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- AlicenseNot gradedqualityFmaintenanceIndexes codebases into Qdrant for semantic search, enabling AI assistants to find relevant code by meaning without re-exploring the repo.MIT
- AlicenseNot gradedqualityDmaintenanceLocal-first semantic memory server with project indexing for AI assistants. It enables AI assistants to store, retrieve, and search memories and project code using embeddings and vector search.55 npmMIT
- AlicenseNot gradedqualityAmaintenancePersistent knowledge memory layer for AI agents. Hybrid semantic + full-text search with pgvector, code dependency graph with blast-radius impact analysis, and incremental indexing for 7 languages. In-process ONNX embeddings, no external API required.24 npm35MIT
- AlicenseAqualityAmaintenanceSemantic codebase search + persistent working memory for AI code editors. Local, zero-config, MCP. No API key.823206 PyPI2MIT
- AlicenseNot gradedqualityAmaintenanceGives AI coding assistants persistent project memory and semantic code search, running fully locally with no API keys required.MIT
- FlicenseNot gradedqualityCmaintenanceProvides persistent codebase memory and semantic context for AI agents via AST-aware chunking and symbol graph indexing.1-
TDQS
Scored across 6 tools
Each tool targets a distinct function: grep searches code lines, list navigates files, retrieve fetches known documents, rules manages preferences, search discovers content, and store ingests content. There is no functional overlap.
All tool names are single lowercase verbs (grep, list, retrieve, rules, search, store), following a consistent and predictable pattern.
With 6 tools covering searching, navigation, retrieval, storage, and rule management, the count is well-scoped for a workspace knowledge server without being excessive or sparse.
The tool surface covers the core workflows of searching, browsing, retrieving, storing, and managing rules. One minor gap is the lack of explicit update/delete operations for stored documents, though store may allow overwriting.