Access Massive.com market data through MCP tools for endpoint discovery, API calls, SQL analysis, and financial calculations.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β or use 1-click editor setup below.
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π‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
Inspect callable tools, capabilities, and parameters exposed to AI agents by MCP Polygon).
search_endpointsSearch for API endpoints and built-in functions by natural language query. Returns titles, path patterns, and descriptions. Set `detail` to `"more"` for query parameter docs, or `"verbose"` for full documentation. Use `max_results` to limit results.
call_apiCall any Massive.com REST API endpoint. Supports storing results as an in-memory database table (`store_as`) and applying post-processing functions (`apply`). Paginated responses include a next-page hint.
query_dataRun SQL against stored SQLite DB. Supports `SHOW TABLES`, `DESCRIBE <table>`, `DROP TABLE <table>`, CTEs, window functions, and more. Results can also be post-processed with `apply`.
The polygon-io/mcp_polygon) MCP server provides an MCP interface to the full Massive.com financial data API. Its data coverage includes stock, options, forex, crypto, and futures aggregates; historical and real-time trades and quotes; snapshots; ticker reference data; dividends; splits; IPOs; fundamentals; analyst ratings; news; treasury yields; inflation data; and market status information.
Rather than defining a separate MCP tool for every financial endpoint, it exposes a smaller set of composable operations. An agent can discover an endpoint, call it with the required parameters, save the response for later analysis, and query the saved data with SQL.
search_endpoints searches the dynamically indexed API documentation and built-in functions using natural language. Results can include endpoint titles, path patterns, descriptions, query parameter details, or full documentation depending on the requested detail level. The index is loaded from Massive.com's llms.txt document at startup.
call_api sends requests to Massive.com REST endpoints. Responses may be stored as in-memory SQLite tables with store_as, and post-processing can be applied through the apply parameter. Paginated responses provide a hint for retrieving the next page.
query_data executes SQL against stored tables. Supported operations include table inspection and deletion, common table expressions, window functions, and other SQLite queries. Results can also be passed through built-in post-processing functions.
The polygon-io/mcp_polygon) MCP server requires Python 3.12 or newer, Astral UV 0.4.0 or newer, and a Massive.com API key. The documented installation uses UV to install the mcp_massive package from the project's Git repository. Claude Desktop can then launch the installed mcp_massive binary over the default STDIO transport.
Set MASSIVE_API_KEY to authenticate requests. POLYGON_API_KEY remains available as a deprecated alias. Optional settings can change the transport, API base URL, endpoint-index URL, maximum number of in-memory tables, and maximum rows per stored table. Supported transports are STDIO, SSE, and streamable HTTP; STDIO is the default.
The polygon-io/mcp_polygon) MCP server is marked experimental, so breaking changes may occur. It depends on access to the Massive.com API and its applicable terms and privacy policy. Stored data is held in an in-memory SQLite database, with configurable limits of 50 tables and 50,000 rows per table by default. The README states that the server does not store or cache user data and proxies requests to Massive.com.
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