Skip to main content
Glama

Related Servers

Alternatives to MARKET_AI_HUB

No user-submitted related servers found.

    Related Servers

    • A
      license
      A
      quality
      A
      maintenance
      Enables AI agents to operate a local financial terminal, including market data, backtesting, paper portfolio management, and news digest, through safe, gated tools over MCP.
      6
      MIT
    • A
      license
      C
      quality
      D
      maintenance
      Provides comprehensive Taiwan stock market data and analysis through MCP tools. Enables querying real-time stock prices, historical data, company information, technical analysis, and market overviews for TWSE and TPEx listed companies.
      8
      16
      MIT
    • F
      license
      Not graded
      quality
      C
      maintenance
      Provides AI clients access to TdxQuant/通达信 financial data and trading capabilities through MCP. Enables retrieval of market data, financial reports, sector information, and trading operations via stdio or HTTP/SSE connections.
      29
      -
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables AI agents to perform quantitative research and backtesting for China A-shares and futures, including data fetching, backtesting, factor analysis, and experiment tracking through MCP tools.
      1
      Apache 2.0
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables AI assistants in MCP clients to query real-time market data, inspect portfolio and Web3 wallet balances across brokers, place protected equity/ETF, crypto, forex, options, DeFi swap, and prediction market orders, and run backtests.
      MIT

    TDQS

    C2.9/5.0

    Scored across 21 tools

    Disambiguation3/5

    Most tools target distinct resource/action pairs, but several status/snapshot tools overlap (health_check vs get_system_info, get_data_source_status vs get_official_release_snapshot vs get_data_coverage), and analyze_taiwan_stock/analyze_osaka_nikkei vs get_analysis_packet have unclear boundaries. The detailed descriptions help, but the tool set is not immediately unambiguous.

    Naming Consistency4/5

    The dominant patterns get_<noun>, predict_<model>, and analyze_<market> are consistent and readable. Minor deviations like health_check, backtest, and run_ts_validation break the pattern slightly but do not create serious confusion.

    Tool Count3/5

    21 tools is on the heavy side for an MCP surface, with several status/coverage/snapshot tools that could plausibly be consolidated. The broad hub scope makes the count defensible, but it is borderline and requires agents to absorb a large tool list.

    Completeness4/5

    The surface covers health, data ingestion, prediction, backtesting, time-series validation, market analysis, and monitoring/archive status. Minor gaps exist around triggering forward tests and retrieving raw prediction outputs, but the core workflows are represented without major dead ends.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues