massive_query_data

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

Annotations declare destructiveHint=false, but the description includes DROP TABLE, which is destructive. This is a contradiction, severely impacting transparency. The description also doesn't clarify other behavioral traits beyond what annotations provide.

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?

The description is two sentences, front-loaded with the main purpose, and every sentence adds specific value. No wasted words.

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?

With no output schema, the description could explain return format or error handling. It adequately covers the tool's capabilities but misses some completeness for an agent to fully understand the output.

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 is 100%, but the description adds value by explaining 'Use apply for server-side Greeks and technicals', providing meaningful context beyond the schema's generic description.

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?

The description clearly states 'SQL queries over stored DataFrames from massive_call_api' and lists specific supported commands (SHOW TABLES, DESCRIBE, DROP TABLE, full SQL). It distinguishes from siblings by specifying the tool's scope, though it doesn't explicitly differentiate from all sibling tools.

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?

The description implies when to use the tool (for SQL queries over DataFrames) but does not explicitly state when not to use it or mention alternatives. The context of 'massive_call_api' provides some guidance, but it's not comprehensive.

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