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    Historical stock pattern intelligence for AI agents. Search 24M pre-computed chart pattern embeddings across 15K stocks and 10 years. 19 tools: pattern similarity search, forward returns, regime analysis, anomaly detection, sector rotation, earnings reactions, correlation shifts, scenario analysis, and more. Returns what happened historically when charts looked like this — compliance-safe
    22
    637 PyPI
    20
    MIT
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    An MCP server that extracts energy-usage charts from utility-bill PDFs, returning structured time-series data and annotated images.
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    AGPL 3.0
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    Enables MCP clients like Claude to query Chinese A-share market data, including K-line charts with forward/backward adjustment, limit-up and limit-down pools, per-stock fund flows, index quotes, and stock news, with automatic symbol normalization and retry handling for throttled data sources.
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    MIT
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    Provides A-share (Chinese stock market) quantitative analysis through tools for stock screening, northbound capital flow tracking, dragon-tiger list analysis, margin trading, sector analysis, technical indicators, IPO info, and limit-up/down statistics using akshare data.
    11
    1
    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
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    Apache 2.0
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    Enables async-first working practices by providing tools to draft decision docs, convert meetings to async artifacts, score status updates, and triage sync vs async tasks. It also offers reference tools for the Open and Async book's principles and coaching prompts.
    11
    55 npm
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    Read-only MySQL MCP server that lets AI agents list tables, describe schemas, and run SELECT/SHOW/EXPLAIN queries with a row cap, bound to a single database for safety.
    3
    MIT
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    A local MCP server for managing a personal kanban board with SQLite, enabling task creation, editing, and movement across backlog, in progress, testing, and done columns.
    7
    MIT
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    MCP server that exposes GDB debugging as tools. An AI assistant can set breakpoints, run programs, step through code, inspect variables and memory, and examine registers — all via structured tool calls. Reverse debugging with rr is also supported.
    34
    3
    MIT