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"How to run Python code" matching MCP servers:

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    A local MCP server that provides semantic code search for Python codebases using tree-sitter for chunking and LanceDB for vector storage. It enables natural language queries to find relevant code snippets based on meaning rather than just text matching.
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    An MCP server designed to assist with generating, converting, and translating Milvus SDK code by retrieving relevant documentation and snippets. It supports PyMilvus code generation, ORM-to-client conversion, and cross-language translation between Python, Java, Go, and other supported languages.
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    MIT
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    A smart code retrieval tool based on Model Context Protocol that provides efficient and accurate code repository search capabilities for large language models.
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    Semantic memory for AI builders: capture the tacit engineering know-how that never reaches your docs, recall it the moment it applies. Built in Rust on Postgres and pgvector.
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    Enables local semantic search over documents and code for Claude Code and Claude Desktop, running entirely offline with local embeddings and vector storage.
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    MIT
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    Python MCP server for vector search using Qdrant vector database and Ollama embeddings, with advanced query techniques like query expansion, HyDE, and reranking.
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    MIT
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    Model Context Protocol server for RosalindDB, enabling AI clients to create datasets, ingest vectors, run similarity queries, and check usage on a cost-optimized vector search database.
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    Apache 2.0
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    A persistent semantic memory system for Claude Code, using vector search and a judgment ledger to surface prior decisions and calibrate predictions.
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    MIT
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    A server that provides access to Baidu Cloud Vector Database functionality through the Model Context Protocol, enabling LLM applications to perform vector searches and database operations via natural language.
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    Apache 2.0
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    Enables GitHub Copilot to query local ChromaDB instances to retrieve relevant documents and context for AI conversations. It allows users to search vector collections using natural language tools directly within VS Code.
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