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  • A
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    MCP server for Microsoft Advertising (Bing Ads) REST API enabling agent-led campaign management and reporting. It provides tools to manage campaigns, ad groups, keywords, ads, budgets, and pull performance reports.
    66
    MIT
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    Integrates PropellerAds' programmatic advertising platform with AI to automate campaign management, real-time optimization, and performance analysis. It enables users to create, scale, and manage ad campaigns through natural language commands for tasks like bid adjustment and zone blacklisting.
    21
    2
    MIT
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    Enables AI assistants to manage Microsoft Advertising accounts, including campaigns, ad groups, keywords, audiences, and reporting, with safety-first write controls such as opt-in mutations, draft-and-confirm workflows, spend ceilings, and audit logging.
    47
    1
    MIT
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    Enables Claude to remember information about users across conversations by storing and retrieving data as entities, relations, and observations in a persistent local knowledge graph.
    315,072 npm
    MIT
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    Generates AI-powered clarifying questions for user task requests using Claude, helping gather detailed requirements about tech stack, architecture, deployment, and project scope through categorized, contextually relevant questions.
    MIT
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    Enables an AI assistant to dynamically discover and invoke MCP tools for document search, employee information lookup, and mathematical calculations, with human-in-the-loop approval for sensitive operations.
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    Enables agents to get fast, calibrated probabilistic answers from Jev (Typesafe AI) to yes/no, scale, or choice questions about provided material, without using a generative model.
    1
    8 npm
    1
    MIT
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    Enables LLMs to communicate with Agent-to-Agent (A2A) protocol compliant agents, providing tools for sending messages, managing tasks, and retrieving agent information.
    4
    8 npm
    MIT
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    Provides versioned, structured memory for AI agents, allowing them to store facts, detect conflicts, and track knowledge history via a hosted SaaS platform. It enables efficient hierarchical information retrieval and semantic search while keeping token usage constant as memory scales.
    7
    32 npm
    8
    Apache 2.0
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    Provides AI assistants with long-term semantic memory capabilities through local vector-based storage. Enables storing, recalling, and managing information across sessions with complete privacy using ChromaDB, with no data ever leaving your machine.
    3
    9
    MIT
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    Gives LLMs a genuine in-game seat in Civilization V, letting them scout, settle, negotiate, fight, and manage a civilization through tool calls with the same fog-of-war information a human player sees.
    4
    9
    1
    MIT
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    Enables LLM assistants like Claude to interact with Zaif cryptocurrency exchange through natural language, supporting market information retrieval, chart data, trading, and account management for BTC/JPY, ETH/JPY, and XYM/JPY pairs.
    9
    MIT
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    Lets MCP clients ask typed questions — yes/no, choice, or score — about structured state, including images, and receive calibrated probabilities from System One decision models such as TypeSafe Jev and Cloudflare Clef. Supports TypeSafe, OpenRouter, and local providers selectable per request, with input validation and cost reporting.
    6
    234 npm
    MIT
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    An MCP server that enables AI agents to pause and request human approval or information via Slack, Telegram, or macOS dialogs before proceeding with actions.
    2
    15
    Apache 2.0
  • F
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    Lets an AI agent ask real humans a question when the web genuinely has no answer, and get verbatim replies back with a respondent count. Built for the moment an agent is about to say "you should validate this with real users". Remote MCP, plain POST, or a single GET. No signup, no API key.
    2
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    Enables coding agents to request calibrated probability judgments over typed questions—yes/no, named choices, or ordered scores—about a described situation, returning one probability per option rather than free text. Runs Cloudflare's Clef model locally on Apple Silicon Macs, with images or video frames optionally attached, and answers delivered as a single MCP tool call.
    1
    83 PyPI
    MIT
  • F
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    Enables AI assistants to play Pokemon Fire Red through the mGBA emulator by providing tools for button inputs and screenshots. It allows for direct reading of real-time game state from RAM, including party information, player location, and battle status.
    10
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    Enables agents to ask TypeSafe AI's Jev decision model typed questions — choices, scores, true/false propositions, and image bounding boxes — about a text or JSON state, returning structured answers with calibrated probabilities and confidence instead of free-form prose. Supports images, multi-question batch calls, and long-context requests over stdio.
    6
    52 npm
    MIT
  • A
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    MCP server for the FLOX trading framework. About 30 tools to run backtests, scaffold strategies, validate for lookahead bias, compute indicators, place orders, and query PnL from Claude/Cursor.
    38
    231
    MIT
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    An MCP server for managing agent memory using provenance tracking, decay-weighted retrieval, and feedback loops to optimize information recall. It allows agents to store insights in a local SQLite database and rank them based on confidence, age, and usefulness.
    4
    50 npm
    MIT
  • A
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    An MCP server that answers typed noul/choice/score questions about a given state with probability distributions and confidence, for routing, escalation, and gating decisions. It runs on pluggable backends (local Needle 3 or Laya models, or any OpenAI-compatible endpoint) and logs every decision so recorded ground truth can be used to track calibration.
    3
    MIT
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    Two-layer memory for AI agents. Episodes compress into identity. The only MCP memory server with an immune system. Patterns earn permanence through evidence, false knowledge gets caught and demoted, and stale information fades — so your agent's memory gets smarter over time, not just bigger. Zero dependencies. 5 tools. Works with any MCP client.
    17
    8,874 PyPI
    12
    MIT
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    Judgment calls on small, fast, cheap models: yes/no, pick-one and rating questions about any text, answered YES, NO or UNSURE. Four tools (likely, classify, rate, each) for TypeSafe's Jev, open models on Ollaya, Ollama or a local NLI model.
    4
    25 PyPI
    7
    Apache 2.0
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    Provides human-like memory dynamics for AI assistants where memories naturally fade over time unless reinforced through use, mimicking the Ebbinghaus forgetting curve. Enables automatic saving, searching, and management of contextual information with temporal decay algorithms.
    11
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    AGPL 3.0
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    Enables AI assistants like Claude to interact with humans through intuitive GUI dialogs, supporting text input, choices, confirmations, and information displays.
    6
    15 PyPI
    163
    MIT
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    Enables AI agents to record and rank learnings, facts, and methods through a collaborative voting framework. It provides tools for agents to surface the most useful information across sessions using persistent memory storage.
    8
    MIT
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    A Model Context Protocol server providing LLMs with access to the Ensembl genomics database, enabling AI assistants to query gene information, sequences, variants, and other genomic data across multiple species.
    10
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    MIT
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    Provides persistent memory capabilities for AI assistants, enabling storage, recall, and analysis of information across conversations with intelligent memory management.
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    14 npm
    MIT
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    Enables AI agents to join 250game-server rooms as ordinary players over native WebSocket without modifying the game server, supporting room setup, card play and manipulation, scoring, and turn-based decisions while filtering hidden information and rejecting stale-state actions.
    16
    MIT
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    An MCP server that enables LLMs to discover, reason about, compose, and execute sktime estimator workflows via a registry-driven semantic engine.
    26
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    BSD 3-Clause
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    A server that enables AI assistants to interact with Linear's project management tools through the Model Context Protocol, supporting features like searching, creating, and updating issues, adding comments, and retrieving user profiles and team information.
    9
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