get_author_publications

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes key behaviors like fuzzy matching, case-insensitive search, and default values for optional parameters. However, it lacks details on error handling, rate limits, authentication needs, or what happens with low similarity thresholds. The description doesn't contradict annotations, but it's incomplete for a tool with fuzzy matching and multiple parameters.

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 and appropriately sized. It starts with a clear purpose statement, then lists arguments with detailed explanations, and ends with return value information. Every sentence adds value, though the return details could be slightly more concise. It's front-loaded with the core functionality.

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 tool's complexity (fuzzy matching, 4 parameters) and lack of annotations/output schema, the description does a good job of covering key aspects. It explains parameters thoroughly and outlines the return structure. However, it could benefit from more behavioral context (e.g., performance implications, error cases) to be fully complete for an agent's use.

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?

The description adds significant value beyond the input schema, which has 0% description coverage. It explains each parameter's purpose: 'author_name' for full/partial name matching, 'similarity_threshold' as a float between 0-1 for match precision, 'max_results' for limiting output with a default, and 'include_bibtex' for including BibTeX entries. This compensates fully for the schema's lack of 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 clearly states the tool's purpose: 'Retrieve publication details for a specific author with fuzzy matching.' It specifies the verb ('retrieve'), resource ('publication details'), and key behavior ('fuzzy matching'). However, it doesn't explicitly differentiate from sibling tools like 'fuzzy_title_search' or 'search', which might have overlapping functionality.

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. It doesn't mention sibling tools like 'fuzzy_title_search' (for titles) or 'search' (which might be more general), nor does it specify prerequisites or exclusions. Usage is implied by the description but not explicitly stated.

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