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

The description discloses that the grade is persisted back to the thesis row, clarifying that it is a write operation despite readOnlyHint=false. It also notes that scoring is fundamental-only and does not include market-price returns, which aligns with annotations and provides additional context. No contradiction with annotations.

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 well-structured with two paragraphs, front-loading the main purpose and details. It is concise but includes a helpful note about future phases. Every sentence adds value, though the note could be considered slightly extraneous for immediate use.

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 presence of an output schema and the tool's complexity, the description explains the scoring methodology, persistence, and future updates comprehensively. It provides enough information for an AI agent to correctly select and invoke the tool.

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%, so baseline is 3. The description adds value by explaining that thesis_id is obtained from save_thesis or list_theses, and that as_of defaults to today and picks the closest fiscal period. This enhances understanding beyond the 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?

The description states 'Grade a saved thesis against fundamental momentum since its creation' and explains the scoring based on revenue, operating margin, EPS, and OCF, producing a score in [-1, +1]. It clearly distinguishes from sibling tools like score_claim or score_due_theses by specifying it operates on a single saved thesis.

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 on when to use this tool: to grade a thesis based on fundamental changes. It explains the scoring logic for bull, bear, and neutral theses, and mentions re-calling to refresh. However, it does not explicitly mention alternatives or when not to use it, though the context of fundamental-only scoring is implied.

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