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"Using a TypeScript codebase as context for a tool or service" matching MCP servers:

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    An MCP server for searching and downloading books from Library Genesis, supporting EPUB, MOBI, PDF, and more through natural language queries.
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    MIT
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    Read-only MySQL MCP server that lets AI agents list tables, describe schemas, and run SELECT/SHOW/EXPLAIN queries with a row cap, bound to a single database for safety.
    3
    MIT
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    A Model Context Protocol server providing tools for querying A-share stock market data, including historical prices, financial reports, market indices, and macroeconomic indicators.
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    MIT
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    Provides comprehensive A-share (Chinese stock market) data including stock information, historical prices, financial reports, macroeconomic indicators, technical analysis, and valuation metrics through the free Baostock data source.
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    MIT
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    MCP server adapter that exposes A-share stock data tools, prompts, and resources via FastMCP, enabling querying of stocks, K-lines, financials, sectors, and market hot spots through natural language.
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    Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.
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    MCP server that provides OpenRouter model pricing data, enabling price lookups, trending/cheapest lists, and model searches without an API key.
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    A custom MCP tool that integrates Perplexity AI's API with Claude Desktop, allowing Claude to perform web-based research and provide answers with citations.
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    MIT
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    A semantic codebase indexer MCP server that chunks source code, generates embeddings via Ollama, and stores them in Qdrant for natural-language code search.
    10
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    Provides a tool to fetch real-time cryptocurrency prices from the Binance API and logs server activity to file resources. It allows users to query crypto symbols and monitor MCP interactions through natural language interfaces.
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    MIT
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    A Python-based MCP server that enables document-based question answering by processing PDF, TXT, and Markdown files through OpenAI's API. It provides hallucination-free responses based strictly on document content using semantic search and includes a web interface for management.
    MIT
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    Enables document-based question answering using OpenAI's GPT-4 with semantic search and embeddings. Upload PDF, TXT, or Markdown files and get answers strictly based on document content with source attribution and confidence scores.
    MIT
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    A production-minded RAG service for MCP that answers questions over your documents with hybrid retrieval, PII redaction, and source citations, packaged for Docker/Kubernetes.