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

With no annotations provided, the description carries the full behavioral burden and does so well. It discloses that the operation is LLM-powered, validated against taxonomy and checksum/format rules, read-only, output-only to the caller, and never added to the dataset. It also states the character limit, giving the agent essential side-effect and constraint information.

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 dense but efficiently structured: it opens with the core action, then lists output contents, validation details, side-effect guarantee, and input limit. Every sentence earns its place, with no filler or redundancy.

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 transformation tool with no output schema, the description does a good job setting expectations: it names the output format, enumerates entity categories, notes validation, and clarifies that results stay with the caller. It could be more explicit about the exact JSON return shape or error behavior for invalid/over-limit input, but the provided detail is sufficient for correct invocation.

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% for the single parameter, so the schema already documents the 'text' parameter adequately. The description adds value by mentioning the 6000-char limit and examples of acceptable text (article, filing excerpt, notes), but this is marginal enhancement rather than essential compensation for a coverage gap.

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 ('Extract'), a clear resource ('financial entities and relationships from unstructured text'), and an exact output format ('Finance Foundation Schema v0.1 JSON'). It enumerates the entity types and relational elements, making the tool's purpose unmistakable and distinct from sibling tools like search or lookup.

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 clearly implies when to use this tool: when you have unstructured financial text and want structured entities/relationships. It does not explicitly name alternatives or state when not to use it, but the input constraint ('unstructured text') and transform-only behavior provide sufficient context for an agent to choose it over read-only query tools.

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