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massive-com

Polygon-io MCP Server

Official

list_short_interest

Retrieve short interest data for stocks by specifying ticker, settlement date, and other parameters to analyze market trends and positions.

Instructions

Retrieve short interest data for stocks.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
orderNo
paramsNo
settlement_dateNo
settlement_date_gtNo
settlement_date_gteNo
settlement_date_ltNo
settlement_date_lteNo
sortNo
tickerNo
Behavior1/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. The description only states the basic purpose without mentioning any behavioral traits such as rate limits, authentication requirements, data freshness, pagination, error conditions, or what happens when parameters are omitted. This leaves the agent with significant uncertainty about how the tool 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 extremely concise with a single sentence that directly states the tool's purpose. There's no wasted language or unnecessary elaboration, making it front-loaded and easy to parse quickly.

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 (10 parameters, no annotations, no output schema, and 0% schema description coverage), the description is completely inadequate. It doesn't explain what the tool returns, how to use the numerous parameters, any behavioral constraints, or how it differs from similar tools. The agent lacks essential context to use this tool effectively.

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?

The description provides no information about any of the 10 parameters. With 0% schema description coverage, the schema only provides parameter names and types without explaining their meaning or usage. The description doesn't compensate by explaining what parameters like 'settlement_date', 'ticker', 'order', or 'params' do, leaving the agent to guess their semantics.

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 'Retrieve short interest data for stocks' states the basic action (retrieve) and resource (short interest data for stocks), but it's vague about scope and granularity. It doesn't specify whether this returns current data, historical data, or filtered results, nor does it distinguish this tool from sibling tools like 'list_short_volume' or other financial data tools.

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 about when to use this tool versus alternatives. The description doesn't mention any prerequisites, context for usage, or comparisons with sibling tools like 'list_short_volume' or other data retrieval tools in the server. The agent must infer usage from the tool name alone.

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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