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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.
    4
    2
    AGPL 3.0
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    A Windows-optimized server providing universal data analytics for JSON and CSV files through over 32 tools including schema discovery and interactive visualizations. It is specifically designed for seamless integration with Claude Desktop on Windows.
    1
    MIT
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    Enables comprehensive academic research by searching and analyzing papers from PubMed, Google Scholar, ArXiv, and JSTOR. Provides full-text access, citation management, and research organization through five powerful consolidated tools.
    206 npm
    16
    MIT
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    Standalone Model Context Protocol server for PropProfessor, plus a small query CLI for local analysis.
    2
    MIT
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    Enables AI agents to query Google Trends for search-interest signals, returning stable JSON envelopes with calibrated searches/day estimates, relative comparisons across keywords, related and rising queries, regional interest, and country-level Trending Now data. It also surfaces explicit status and error codes so agents can distinguish partial or failed results from real numbers.
    MIT
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    A production-ready MCP server that gives Claude access to the entire DataForSEO API v3 through 51 tools, covering SERP, Labs, Keywords, Backlinks, OnPage, Business, and more, with cost controls and response normalization.
    12,789 npm
    Apache 2.0
  • F
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    Allows users to search for academic conferences and events from WikiCFP by keywords, providing details such as event name, description, dates, location, and submission deadlines.
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    Enables interaction with LimeSurvey API to create and manage surveys, questions, question groups, activate surveys, and export responses through standardized MCP endpoints.
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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.
    7
    66 npm
    MIT
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    MCP server that gives agents the same interface as dashAI's GUI: explore datasets, list models, train, track jobs, and read metrics.
    10
    21 PyPI
    MIT
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    An MCP server that reads a team's interviews, tickets, and docs to build a model of the product, exposing tools to ask questions, list contradictions and gaps, and draft documents, with all answers citing their source fragments.
    6
    11 npm
    MIT
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    Exposes 26 structured analytical tools over Climate Finance Update datasets, enabling fund-level financial analysis, portfolio aggregations, and data quality diagnostics without requiring the client to write data access code.
    26
    Apache 2.0
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    A
    quality
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    maintenance
    MCP server for searching papers from top-tier and second-tier security conferences (S&P, USENIX Security, CCS, NDSS, ACSAC, RAID, ESORICS) using DBLP, with tools for keyword/author/year/conference search, listing conference papers, and getting statistics.
    4
    10 npm
    MIT
  • A
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    Product management copilot that connects Claude to HelpScout support tickets and ProductLift feature requests. Synthesizes customer feedback across sources, scores themes by convergence, and generates prioritized product plans.
    30
    MIT
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    Enables searching and querying AI Hub datasets, including listing datasets, getting details, searching by keyword, and retrieving download information.
    1
    Creative Commons Attribution Non Commercial 4.0 International
  • F
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    An MCP server for discovering ColabFit materials science datasets and training MACE interatomic potentials on local hardware, enabling AI assistants to search, download, train, and validate models.
    2
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    Enables AI agents to train and query Explainable Boosting Machine models on CSV data, returning transparent predictions with exact feature contribution breakdowns via MCP.
    MIT