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Polygon-io MCP Server

Polygon-io MCP Server

Official
by massive-com
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_futures_product_details

Retrieve detailed information for a specific futures product, including its state on a selected date. Utilize the Polygon-io MCP Server for accurate historical futures data.

Instructions

Get details for a single futures product as it was at a specific day.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "product_code": "<product_code>"
    },
    "name": "get_futures_product_details"
  }
}

Input Schema

NameRequiredDescriptionDefault
as_ofNo
paramsNo
product_codeYes
typeNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.8/5.0
Behavior2/5

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

No annotations are provided, so the description carries full burden. It implies a read-only operation ('Get details') but doesn't disclose behavioral traits like authentication needs, rate limits, error handling, or what 'details' include (e.g., fields, format). For a tool with 4 parameters and no output schema, this leaves significant gaps in understanding how it behaves.

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?

The description is a single, efficient sentence with zero waste—front-loaded and to the point. Every word contributes to conveying the tool's purpose without redundancy or fluff.

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

Completeness2/5

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

Given the complexity (4 parameters, 0% schema coverage, no annotations, no output schema), the description is incomplete. It lacks details on parameter usage, behavioral context, output format, and differentiation from siblings, making it insufficient for an agent to invoke the tool confidently.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate but adds no parameter information. It mentions 'a specific day' which loosely relates to 'as_of', but doesn't explain 'product_code', 'params', or 'type', leaving 3 of 4 parameters undocumented. This fails to bridge the coverage gap adequately.

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

Purpose4/5

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

The description clearly states the action ('Get details') and target resource ('a single futures product'), with the temporal constraint 'as it was at a specific day' adding specificity. However, it doesn't explicitly differentiate from sibling tools like 'get_futures_contract_details' or 'get_futures_snapshot', which likely serve related but distinct purposes.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives. With many sibling tools (e.g., 'get_futures_contract_details', 'get_futures_snapshot', 'list_futures_products'), the description lacks context about prerequisites, typical use cases, or comparisons to help the agent choose appropriately.

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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