Extract keywords

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

Beyond the readOnly/idempotent annotations, the description discloses meaningful behavior: stopwords and very short tokens are filtered out, and the output includes counts and relative frequency. This adds useful behavioral context without contradicting the 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 uses three efficient sentences, front-loading the core behavior first and following with purpose-driven use cases. Every sentence adds value and there is no redundant restatement of the tool name or title.

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 simple, read-only extraction tool, the description is complete: it explains the output, filtering behavior, and practical applications. The presence of an output schema and full parameter coverage means no essential calling context is missing.

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 the schema fully documents all three parameters. The description adds context about 'meaningful words' and stopword filtering, but does not meaningfully deepen parameter-level semantics beyond what the schema already provides, so the baseline of 3 applies.

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 operation: 'Return the most frequent meaningful words in the text, with counts and relative frequency.' This clearly identifies the resource (text), the output (keywords with frequency), and naturally distinguishes it from siblings like extract_entities or summarize_text.

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 concrete use cases: 'tagging, SEO checks or spotting what a document is actually about.' However, it does not explicitly mention when not to use this tool or name alternatives, though the use-case guidance is clear enough for an agent to select it appropriately.

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