ner_extract

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'confidence scores' which adds some context about output behavior, but fails to address critical aspects like whether this is a read-only operation, potential rate limits, authentication requirements, error conditions, or what happens with invalid input. For a tool processing sensitive clinical data, this is a significant gap.

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 efficiently structured in two sentences that directly convey core functionality. The first sentence states the primary purpose, while the second adds specific details about what gets extracted. There's minimal waste, though it could be slightly more front-loaded by integrating the entity type examples into the main purpose statement.

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 medical entity extraction tool with no annotations and no output schema, the description is insufficient. It doesn't explain the return format (structure of extracted entities with confidence scores), doesn't address privacy/security considerations for clinical data, and provides minimal behavioral context. Given the complexity and sensitivity of medical text processing, more comprehensive guidance is needed.

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 already documents both parameters thoroughly. The description adds marginal value by implying the 'entityTypes' parameter can filter to specific types mentioned (ICD-10, CPT, etc.), but doesn't provide additional syntax, format details, or examples beyond what the schema provides. Baseline 3 is appropriate when 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 verb 'extract' and resource 'medical named entities from clinical text', with specific examples of what gets extracted (ICD-10 codes, CPT codes, dates, medications, and 12 entity types). It distinguishes from sibling tools by focusing on entity extraction rather than validation, lookup, enrichment, or other operations present in the sibling list.

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 no guidance on when to use this tool versus alternatives. While it implies usage for clinical text analysis, it doesn't specify scenarios where other tools like code_lookup, drug_enrich, or provider_search might be more appropriate, nor does it mention prerequisites or constraints for effective use.

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