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

Annotations already establish that this is a safe, read-only, closed-world operation. The description adds meaningful behavioral context beyond annotations: the summary is AI-generated, may contain errors, is processed by Kabuken rather than being raw FSA data, and is not investment advice.

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 core purpose is front-loaded and easy to find, but the description repeats EDINET and AI-generated provenance across multiple sentences and includes licensing URLs that do not help an agent select or invoke the tool. It is adequately structured but not maximally economical.

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?

For a read-only two-parameter tool with a fully documented schema and no output schema, the description is largely complete. It explains the provenance and caveats of the returned summary, though it could say more about the summary's expected form or 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?

Schema description coverage is 100%, so both the doc_id and language parameters are already well documented in the schema. The description does not add syntax, format, or behavioral meaning for either parameter beyond what the schema provides.

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 and resource: retrieve an AI-generated comprehensive summary of a Japanese financial document from EDINET. It clarifies that the output is a processed summary rather than raw filings, which helps distinguish it from data-fetching siblings, but it does not explicitly name an alternative tool.

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 gives no explicit guidance on when to use this tool versus alternatives such as analyze_document, get_financial_facts, or list_documents. The intended use is only implied by the fact that it returns a summary rather than raw data.

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