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    A Python implementation of the Model Context Protocol that enables applications to provide standardized context for LLMs, allowing developers to build servers that expose data and functionality to LLM applications.
    MIT
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    A Python port of the official Perplexity MCP server that enables web-grounded Q\&A, deep research, and step-by-step reasoning via Perplexity AI's models. It supports both STDIO and HTTP interfaces for seamless integration with tools like Claude Desktop and Cursor.
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    A Python-based server providing persistent memory management for AI models with SQLite and Markdown dual backend storage. It features full-text search, RAG-enhanced querying, and cross-project knowledge sharing for integration with Claude, Cursor, and Rovo Dev.
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    Enables retrieval and cleaning of official documentation content for popular AI/Python libraries (uv, langchain, openai, llama-index) through web scraping and LLM-powered content extraction. Uses Serper API for search and Groq API to clean HTML into readable text with source attribution.
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    A Python-based SQLite MCP server supporting database read/write operations with natural language interaction capabilities, packaged for Docker deployment in stdio mode.
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    Rust-powered PDF toolkit over MCP: create, read, and analyze PDFs; extract text and entities for RAG; convert to Markdown; split/merge/rotate/reorder pages; manage form fields and annotations; encrypt documents. Runs locally via uvx oxidize-mcp.
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    MIT
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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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    MCP Long Context Reader is a Python-based toolkit designed to overcome the context window limitations and high costs associated with Large Language Models (LLMs) processing extensive documents. It provides a FastMCP server with multiple, powerful strategies for an LLM agent to 'read' and query long documents without needing to load the entire text into its context window.
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    MIT
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    Provides a read-only interface to audit and continue coding agent sessions by extracting plans, intents, and edit authorship from history across multiple agents (Claude, Codex, OpenCode, Antigravity, Pi) via MCP, CLI, and Python SDK.
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    MIT
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    Deterministic code-graph (GraphRAG) over your repo for LLM agents — local-first, git-native, zero-infra, served via MCP. Python, TS/JS, Rust, Go, Java, C#.
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    Apache 2.0
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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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    Generates crash-proof Python LLM pipelines from plain English descriptions. Pipelines resume from the last successful node after a crash instead of restarting from scratch. IProvider-agnostic , works with Groq, Gemini, Ollama, OpenAI, or any Python callable. Zero infrastructure, runs entirely on free-tier APIs.
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    MIT