icp_deep_dive

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does not state any side effects (likely a read/analysis operation, but that is implied), does not mention whether it performs clustering, scoring, or just descriptive analysis, and does not indicate the output format (though no output schema exists). The description does not disclose what happens with missing or incomplete data.

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 a single sentence that is concise and front-loads the primary purpose. It uses efficient phrasing to list the key dimensions. No extraneous information is included, though it could have used the space to clarify usage without becoming verbose.

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?

Given the tool has no annotations and no output schema, and the siblings indicate a rich functional context (scoring, gap analysis, lookalike generation), the description is under-specified. An agent cannot determine what the output looks like, whether it clusters customers or scores them, or what to do with the results. The input schema is rich but the description does not explain how to leverage the attributes for analysis.

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?

The schema description coverage is 100%, so each parameter has a description. However, the descriptions are minimal and do not explain the relationship between 'customers' and 'customer_descriptions' (e.g., whether they are mutually exclusive, which takes priority). The description of the tool itself lists attributes like 'firmographics', which partially appear in the schema (e.g., industry, size), but does not clarify the exact expected format of customer_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?

The description states a clear verb (analyze) and resource (customer data) and lists specific attributes to detect ICP patterns (firmographics, technographics, buying behavior). However, it does not distinguish this tool from siblings like icp_gap_analysis or lookalike_signal_generator, which may also analyze customer data for ICP insights.

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 provides no guidance on when to use this tool instead of the siblings. It mentions analyzing customer data to detect ICP patterns, but does not state exclusions (e.g., when not to use) or alternatives. The input schema offers alternative inputs (customer_descriptions) but the description does not clarify which input to choose based on data availability.

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