massive_call_api

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

Adds useful behavioral context beyond annotations: mentions optional in-memory DataFrame storage and pagination auto-detection. No contradiction with annotations (readOnlyHint, idempotentHint, etc.). Could further disclose memory implications or rate limits.

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

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

Two sentences plus a pagination note—every sentence adds value. No filler, front-loaded with core purpose. Efficiently conveys key information.

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

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

Adequate but leaves gaps: no description of return format, error handling, or how to use the 'apply' parameter in detail. With no output schema, more explanation on response structure would be helpful.

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

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

Schema coverage at 88% is high, but description adds value by explaining 'store_as' parameter for DataFrame storage and pagination note. Also lists examples for 'apply' parameter. Provides context beyond schema descriptions.

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

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

Description clearly states 'Execute a Massive market data API call' with specific resource and verb. Mentions optional DataFrame storage and pagination auto-detection, distinguishing it from sibling tools like massive_get_endpoint_docs or massive_query_data.

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

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

No guidance on when to use this tool versus alternatives. Does not mention when to choose massive_call_api over sibling tools like massive_get_endpoint_docs or massive_query_data, leaving the agent without selection criteria.

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