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"How to increase a model's context window for handling large files" matching MCP servers:

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    Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.
    3
    27
    Apache 2.0
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    An MCP-powered AI agent that audits your Gmail for recurring subscriptions, detects silent price increases, and flags unused services.
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    A personal memory engine and MCP server that stores durable facts in markdown files managed via git, enabling hybrid search (lexical + semantic) through an MCP interface for persistent context across LLM sessions.
    3
    MIT
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    A minimal MCP server that enables HTTP requests with any method, headers, and body types, supporting large responses through chunked transfers with disk-backed caching.
    2
    MIT
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    Obox MCP is a development toolkit that enables AI agents to autonomously execute system commands, manage files, and navigate projects within the IDE.
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    MIT
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    A state persistence layer that enables seamless handoffs between different AI coding assistants by maintaining a shared context bus. It provides tools for tracking summaries, next steps, and active files to ensure continuity across development sessions.
    3
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    Remote-first tmux co-pilot that lets LLMs operate inside real tmux sessions with SSH-aware discovery, deterministic window/pane control, and pull-based state snapshots for grounded terminal assistance.
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    AGPL 3.0
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    Bridges Confluence Wiki with Large Language Models, allowing them to search, read, and explore content through automated HTML-to-Markdown conversion. It supports both local stdio and containerized HTTP/SSE transport modes for flexible deployment.
    3
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    A Model Context Protocol server built with FastMCP that enables dynamic tool loading and configuration from individual Python files. It provides a flexible framework for automatically discovering, testing, and running tools via Stdio or HTTP transport modes.
    1
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    PhysBound is a specialized "Physics Linter" for AI that deterministically validates RF and thermodynamic claims against hard physical limits, preventing hallucinations in engineering workflows.
    4
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    MIT
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    MCP server for business-day arithmetic with country-aware holiday calendars. It offers tools to check, calculate, and list business days and holidays for over 60 countries.
    9
    MIT
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    Read-only MCP server for the Bucharest Stock Exchange, exposing instrument data, OHLCV candles, and fundamentals through a public backend with no authentication.
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    MIT
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    Code-mode MCP server (docs_search + execute_code two-tool surface) backed by a unified capability manifest across three sandbox kernels (in-process node:vm, WASM via QuickJS / Pyodide / Wasmtime, and remote microVM via E2B / Cloudflare Sandbox). At N=30 tools the bootstrap-context cost drops to 13.6% of direct tool-use. Apache-2.0.
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    Apache 2.0
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    Enables AI assistants to query, analyze, and discover FileMaker database schemas via OData v4, with pandas-powered analytics for efficient data summaries.
    13
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    GPL 3.0