get_scoring_history

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 describes the return format ('structured records and a compact text format for prompt injection') and the purpose ('calibrate future scoring runs'), but it does not address potential limitations like rate limits, authentication needs, or data freshness. It adds some context but leaves gaps in behavioral traits.

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 appropriately sized and front-loaded, with three sentences that each earn their place: the first states the purpose, the second details the return content, and the third provides usage context. There is no wasted text, and it efficiently conveys essential 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?

Given the tool's moderate complexity (fetching historical data with two parameters), no annotations, and no output schema, the description does a good job of covering purpose, return format, and usage context. However, it could be more complete by addressing potential errors, data scope (e.g., time range), or integration details, leaving minor gaps in contextual coverage.

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 the schema already fully documents the two parameters (entityName and limit). The description does not add any additional meaning or syntax details beyond what the schema provides, such as examples of entity names or clarification on the default limit. Baseline 3 is appropriate when the schema does the heavy lifting.

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 the specific action ('Fetch scoring history'), the resource ('entities from the Knowledge Graph'), and the scope ('past scoring decisions with DMF scores and dates'). It distinguishes this tool from siblings by focusing on historical scoring data rather than current operations or workflow management.

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 clear context for when to use this tool ('to see how entities were previously scored and calibrate future scoring runs'), but it does not explicitly mention when not to use it or name specific alternatives among the sibling tools. The guidance is helpful but lacks explicit exclusions.

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