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"Testing subscribe functionality on a Python server" matching MCP servers:

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    A resilient MCP server wrapping FiinQuant/FiinQuantX Python SDK for Claude Desktop and Cursor, providing market data, fundamental analysis, screening, and technical indicators via stdio with timeouts, error envelopes, and free-tier guards.
    Last updated
    26
    MIT
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    A Model Context Protocol server that provides tools to fetch weather alerts for US states and forecasts based on latitude/longitude coordinates using the US National Weather Service API.
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    2
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    1
    MIT
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    Provides comprehensive access to SQL Stream Builder (SSB) functionality with 80+ MCP tools for managing streams, jobs, tables, and real-time data processing. Supports both standalone SSB deployments and Cloudera Data Platform with multiple authentication methods.
    Last updated
    82
    4
    Apache 2.0
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    An MCP server that integrates Verodat's data management capabilities with AI systems like Claude Desktop, enabling users to manage accounts, workspaces, and datasets, as well as perform AI-powered queries on their data.
    Last updated
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    Provides read-only access to MetaTrader 5 market data, trading history, and technical analysis through Python execution. Supports querying price data, calculating indicators, creating charts, and generating forecasts with Prophet and ML models.
    Last updated
    3
    MIT
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    A server that provides Claude with Redis database access and operations through a set of tools for basic operations, lists, hashes, sets, and pub/sub functionality.
    Last updated
    24
    MIT
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    A proof of concept server that provides tools for accessing and managing Loomer data, forms, form responses, and projects with pagination, filtering, and sorting capabilities.
    Last updated
    MIT
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    An MCP server that connects AI agents to cloud-native geospatial data via STAC metadata and DuckDB with H3 spatial indexing, enabling zero-configuration SQL queries on terabyte-scale datasets over S3.
    Last updated
    23
    BSD 3-Clause
  • F
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    Enables the analysis of massive datasets by storing data in external variables via a sandboxed Python REPL instead of the model's context window. This allows users to process files larger than 100MB without polluting the context or hitting token limits.
    Last updated