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"How to implement intent recognition in MCP" matching MCP servers:

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    Enables academic research through paper search across multiple databases (IACR, CryptoBib, Crossref, Google Scholar), PDF processing, and GitHub repository browsing. Features modular architecture with FastMCP-based proxy server routing to specialized academic tools.
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    2
    MIT
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    Read-only MCP server for the July 2026 survey of AI in open-source design systems, enabling agents to query 19 systems' affordances, coercion techniques, and platform data via 9 tools, 2 resources, and 2 prompts.
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    Real human judgment as agent tools -- an AI agent can ask a question and get back a structured, schema-validated JSON answer from a real quality-scored human. 16 response types (yes/no, ratings, rankings, A/B tests, sentiment, image/video/audio review, voice/video/photo capture). Fully programmatic signup with a $5 free trial credit, no card required.
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    MIT
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    This project implements a Model Context Protocol (MCP) server providing Formula One racing data using the Python FastF1 library. Inspired by an existing TypeScript server, it offers similar F1 data functionalities natively in Python via FastF1.
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    MIT
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    Enables AI assistants to load and query local GEDCOM genealogy files, supporting search, browsing, and family tree traversal.
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    AGPL 3.0
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    Scans job boards for keyword patterns that indicate buying intent, technology adoption, or team expansion. Returns structured signal data for outbound targeting.
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    MIT
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    Enables users to select the optimal LLM for their specific task by aggregating benchmark data and user intent, returning a ranked shortlist with plain-English rationale.
    5
    MIT
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    Enables querying WikiTree genealogy data including profiles, ancestors, descendants, relatives, biography, photos, and categories via natural language.
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    AGPL 3.0
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    Provides advanced analytical, research, and natural language processing capabilities through a Model Context Protocol server, enabling dataset analysis, decision analysis, and enhanced NLP features like entity recognition and fact extraction.
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    MIT
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    Track how your brand appears in AI-generated answers across ChatGPT, Perplexity, and other AI models. Analyze visibility, sentiment, citations, and domain rankings with 31 tools β€” including analytics reports, chat inspection, query analysis, and full CRUD for brands, prompts, tags, and topics.
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    MIT
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    Provides structured behavioral analysis grounded in Panksepp's affective neuroscience and pattern matching to understand human intent and behavior through primary emotional systems and transition dynamics.
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    A strategic advisory agent that applies Clayton Christensen's frameworks, such as Jobs-to-Be-Done and Disruption Theory, to analyze business decisions. It enables users to evaluate organizational capabilities, explore historical case studies, and receive theory-grounded strategic insights.
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    MIT
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    Transforms raw Google Search Console signals into actionable marketing insights, such as detecting cannibalization, segmenting search intent, and identifying citation opportunities, for any MCP-compliant AI agent.
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    37
    MIT