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

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    Provides AI assistants with real-time visibility into your codebase's internal libraries, team patterns, naming conventions, and usage frequencies to generate code that matches your team's actual practices.
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    433
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    Elastic 2.0
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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.
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    MIT
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    Run DeepSeek as a real sub-agent inside Claude Code / Codex CLI — not just a single LLM call. DeepSeek gets its own 7-tool agent loop (Read/Write/Edit/Bash/Glob/Grep/NotebookEdit) inside a sandboxed workspace.
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    MIT
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    Deterministic SNAP eligibility logic, exposed as auditable Model Context Protocol tools. An AI agent can read, reason, and orchestrate, but the eligibility determination itself is made by versioned, tested, cited code that a caseworker, an auditor, or a court can inspect.
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    A read-only MCP server that lets a Claude chat explore your local repository and answer questions about it, returning synthesized answers with file:line references.
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    MIT
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    A TypeScript/JavaScript refactoring MCP server that uses the TypeScript compiler to perform safe, type-aware code transformations such as renaming, extracting functions, and organizing imports across your codebase.
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    MIT
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    Delegates work from MCP clients (like Claude Code) to the Codex CLI, allowing spawning of autonomous Codex subagents for tasks.
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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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    An MCP server that preserves LLM context by intercepting large data outputs and returning only concise summaries or relevant sections. It enables efficient sandboxed code execution, file processing, and documentation indexing across multiple programming languages and authenticated CLIs.
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    19,859
    Elastic 2.0
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    A small MCP server that gives agents rich context about a YouTube video — its transcript, jump-to-the-moment deep links, metadata, and most-replayed moments — so they can answer questions, summarize, pull quotes, or surface highlights.
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    MIT
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    Multi-modal RAG engine for AI assistants. Stores conversation history, conclusions, diffs, error traces, and other development artifacts in LanceDB with vector search, multi-factor scoring, and an LLM-driven consolidation pipeline.
    10
    MIT
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    An MCP server that provides utility tools for text analysis, URL metadata fetching, and temperature conversion.
    3
    MIT
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    Context Mode is an MCP server that reduces context window waste by sandboxing data-heavy tools, tracking session state in SQLite, and promoting code-based analysis over raw data reads, achieving up to 98% context savings.
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    19,534
    Elastic 2.0
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    Provides official INDEC register designs and methodological rules to AI models, enabling accurate EPH data analysis code (R/Python) without hallucinations.
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    A local-first MCP server that ingests PDFs, extracts structure, and provides semantic search and sequential navigation tools for AI clients to query and learn from documents.
    10
    MIT
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    Graph-based tool retrieval for LLM agents. Builds a tool graph from OpenAPI/MCP specs and retrieves multi-step workflows via hybrid search (BM25 + graph traversal + embedding), recovering accuracy from 12% to 82% with 79% fewer tokens. Also works as an MCP Proxy to aggregate multiple servers behind 3 meta-tools.
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    16
    MIT
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    An MCP server that enables processing of massive datasets up to 10M+ tokens using a recursive language model pattern for strategic chunking and analysis. It automates sub-queries and result aggregation using free local inference via Ollama or the Claude API to handle context beyond standard prompt limits.
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    1
    MIT
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    Connects AI assistants to Jira Service Management instances, providing 40 tools for managing service desks, requests, comments, customers, organizations, queues, SLA, approvals, and attachments.
    40
    1
    MIT