icp_scoring_model

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

Annotations are absent, so the description carries the full burden. It does disclose a meaningful behavior—criteria are auto-weighted based on success_patterns—but it does not mention side effects, persistence, return values, or what happens with existing models. The core behavior is present, yet the full behavioral picture is incomplete.

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?

A single, front-loaded sentence with no wasted words. The verb, resource, and primary behavior all appear immediately, making the description easy to scan and parse.

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 annotations, no output schema, and three parameters, the description is too thin. It gives no clue about the expected returned model, which parameters are necessary, or how this tool fits with the surrounding ICP analysis workflow. An agent could call it, but would have to make assumptions about product_category and the resulting 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 67%, leaving product_category undocumented in both the schema and the description. The description reinforces success_correlation ('based on your success patterns') and implicitly references scoring_criteria via 'auto-weighted criteria,' but it adds no concrete semantic detail and does not compensate for the missing product_category guidance.

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 specific verb ('Create'), resource ('qualification scoring model'), and a distinguishing mechanism ('auto-weighted criteria based on your success patterns'). This clearly separates it from siblings like icp_deep_dive or account_prioritization, which focus on analysis rather than model creation.

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?

There is no guidance on when to use this tool versus alternatives, nor any exclusions or prerequisites. The phrase 'based on your success patterns' hints at context but does not explain when icp_scoring_model is the right choice over sibling tools.

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