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"A server for executing Python code, with support for MCP" matching MCP servers:

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    MCP server that silently monitors state changes in structured data sources (e.g., government open data) and provides a pull-based channel for AI agents to retrieve real mutations since their last cursor.
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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 users to search and query information about military service alternative companies in South Korea through the Military Manpower Administration (MMA) API. Supports filtering by service type, industry, company size, location, and recruitment status.
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    MIT
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    An MCP server that automates the entire process of discovering, evaluating, and applying for government support programs in South Korea using natural language. It integrates multiple public APIs to provide eligibility checks, document preparation, application timelines, and benefit management tools.
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    Torify gives AI agents the Japanese locale primitives that standard libraries lack — imperial era date conversion (wareki), qualified invoice number validation with NTA registry lookup, corporate number lookup (法人番号), postal code resolution, name romanization (Hepburn), and kanji-to-kana conversion via Yahoo! JLP. 31 endpoints total. No authentication required for MCP. Pay-per-call $0.02/call via
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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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    Connect Claude Desktop or any MCP-compatible client directly to your EDGAR Alert account. Ask live questions about SEC insider trading signals, Form 4 activity, 8-K officer/director changes, and company research context without leaving your conversation.
    Last updated
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    MIT