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polygon-io

Massive.com MCP Server

search_endpoints

Read-only

Describe what financial data you need in plain English to find the matching API endpoint or function. Use this tool first to identify the correct endpoint before calling the API.

Instructions

Search for market data API endpoints and built-in finance functions by natural language query. Use this FIRST to find the right endpoint before calling call_api. Covers stocks, options, forex, crypto, futures, indices, ETFs, and economic data. Pass market to pin results to a specific asset class when you already know it; omit it and the server will infer from the query. Use detail="more" to see query parameter docs needed for building call_api requests.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural language search query for API endpoints
scopeNoSearch scope: "endpoints" for API only, "functions" for local functions only, or "all"/omit for both
detailNoLevel of detail per result. "default": title, path, and description. "more": adds query parameter documentation. "verbose": adds response attributes and sample response.
marketNoOptional market/asset class filter. Omit to infer from the query. Use to pin results to a specific asset class when you already know it.
max_resultsNoMaximum number of results to return (default 5 for mixed, 7 for endpoints-only)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations declare readOnlyHint=true, and description does not contradict. Adds behavioral context like inferring market from query and covering various asset classes, but does not detail return format or pagination.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, all relevant and front-loaded with purpose. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Complete for a search tool: describes what it searches, how to use, and how it connects to call_api. Output schema exists but description still provides sufficient context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but description adds value by explaining how market and detail parameters affect results, e.g., 'Pass market to pin results... omit it and the server will infer.'

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool searches for market data endpoints and functions by natural language query, and explicitly differentiates from sibling call_api by advising to use this first.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says 'Use this FIRST to find the right endpoint before calling call_api.' Provides guidance on when to use market and detail parameters, but does not explicitly mention when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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