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Polygon-io MCP Server

Polygon-io MCP Server

Official
by massive-com
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_snapshot_option

Retrieve snapshot data for a specific option contract by specifying the underlying asset and contract details on the Polygon-io MCP Server.

Instructions

Get snapshot for a specific option contract.

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": {
      "underlying_asset": "<underlying_asset>",
      "option_contract": "<option_contract>"
    },
    "name": "get_snapshot_option"
  }
}

Input Schema

NameRequiredDescriptionDefault
option_contractYes
paramsNo
underlying_assetYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.1/5.0
Behavior1/5

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

No annotations are provided, so the description must fully disclose behavior. It only states the action without details on permissions, rate limits, data freshness, or output format (e.g., real-time vs. historical). For a financial data tool with potential complexity, this is a significant gap in transparency.

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, clear sentence with no wasted words. It's front-loaded and efficiently conveys the core action, though this brevity contributes to gaps in other dimensions. Every word earns its place, making it structurally sound.

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

Completeness1/5

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

Given the complexity (financial data tool with 3 parameters, 0% schema coverage, no annotations, no output schema), the description is severely incomplete. It doesn't address behavior, parameter meanings, or output, failing to provide enough context for effective use by an AI agent.

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

Parameters1/5

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

Schema description coverage is 0%, so parameters are undocumented in the schema. The description adds no meaning beyond the tool name, failing to explain what 'option_contract', 'underlying_asset', or 'params' represent (e.g., ticker symbols, configuration options). This leaves parameters ambiguous and unusable.

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

Purpose3/5

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

The description states the verb 'Get' and resource 'snapshot for a specific option contract', which clarifies the basic purpose. However, it's vague about what a 'snapshot' entails (e.g., price data, volume, Greeks) and doesn't differentiate from sibling tools like 'get_snapshot_all' or 'get_snapshot_ticker', leaving ambiguity about scope.

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_snapshot_all', 'get_futures_snapshot'), the description lacks context for selection, such as specifying it's for options only or comparing to other snapshot tools. This omission reduces usability in a crowded toolset.

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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