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    A neutral, non-custodial payment router for AI agents: it discovers which payment rails a paid endpoint accepts (x402 on Base/EVM, Solana, MPP), then pays across them using the caller's own wallet key. Three tools — discover_payment_rails, pay_for_resource, payment_history.
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    MIT
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    Enables semantic search and analysis of customer support tickets. Provides tools to search tickets, analyze the dataset, and retrieve individual tickets using natural language.
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    MIT
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    Enables searching 12306 railway ticket information, filtering trains, querying passing stations, and searching transfer routes through MCP.
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    3,046 npm
    MIT
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    A read-only MCP server exposing TOTVS support tickets (Portal do Cliente) as tools for LLMs in Claude Code, Claude Desktop, and Cursor.
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    MIT
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    Provides access to Saudi passenger rail schedules and fares, including Haramain High-Speed Railway and intercity East/North lines, enabling natural language queries for train times and prices.
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    MIT
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    Enables users to describe their LLM fine-tuning job once and get the cheapest, fastest, and most balanced GPU options across a dozen cloud providers in seconds.
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    104
    MIT
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    Enables AI agents to perform local video, audio, and file operations inside an isolated workspace, including cutting/concat videos, extracting audio, transcribing, and managing files, with typed responses and background job support.
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    Gives LLM agents spatial awareness inside Autodesk Maya, enabling scene inspection and measurement, aesthetic and engineering audits, camera planning, checkpoint/rollback safety, asset import, and viewport or render previews. Agents can also execute arbitrary Python in Maya to build and verify 3D scenes against engineering rules instead of writing blind.
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    97 PyPI
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    MIT
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    Provides AI assistants with specialized tools to interact with NIST's Open Security Controls Assessment Language (OSCAL) framework. It enables agents to retrieve schemas, explore models, and generate valid OSCAL documentation for security compliance automation.
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    Apache 2.0
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    Enables AI-driven 3D modeling and scene creation in Blender, allowing LLMs to create, modify, and manipulate objects, materials, and assets through natural language.
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    MIT
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    Enables AI agents to browse and filter experiment runs, retrieve metrics, logs, tables, and artifacts, plot comparisons as PNG images, and download full-fidelity training history for local analysis against a self-hosted Open Train server. It also provides diagnostic tooling that surfaces metric evidence and investigation leads without asserting causality.
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    MIT
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    Enables AI agents to invoke a local Stata installation, including a Windows Stata build under Wine on Linux and macOS, for regression analysis, econometrics, paper replication, and empirical research with command guards, resource monitoring, and automatic log capture.
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    AGPL 3.0