OpenAlex Author Disambiguation MCP Server

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

Annotations already provide readOnlyHint=true and openWorldHint=true, indicating safe read operations with external data. The description adds valuable behavioral context beyond annotations: it specifies the tool analyzes 'recent works' with a sample size limit (default:5, max:10), describes what information is extracted (institutional keywords, name variants, email addresses), and outlines the return structure. No contradiction with annotations exists.

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 front-loaded with the core purpose, followed by organized sections for Args, Returns, and Example usage. Every sentence earns its place by providing essential information without redundancy, making it efficient for an AI agent to parse.

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 (analyzing author samples with institutional data), the description is complete: it covers purpose, parameters, return values, and usage example. With annotations covering safety and an output schema presumably detailing the return dict structure, the description provides all necessary contextual information without needing to explain technical output details.

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?

With 0% schema description coverage, the description fully compensates by providing detailed parameter semantics: it explains 'author_name' with examples ('Ivan Matic', 'J Smith') and 'sample_size' with default value, maximum limit, and purpose ('Number of recent works to analyze in detail'). This adds significant meaning beyond the bare schema.

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 specific action ('Get detailed author sample from PubMed') and resource ('with institutional information'), distinguishing it from sibling tools like 'search_pubmed' or 'search_authors' by focusing on detailed analysis rather than basic search. The example usage reinforces this specific purpose of obtaining an institutional profile.

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 implies usage context through the example ('Get institutional profile for author') and parameter descriptions, but does not explicitly state when to use this tool versus alternatives like 'retrieve_author_works' or 'get_orcid_publications'. It provides clear input guidance but lacks explicit comparison to sibling tools.

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