OpenAlex Author Disambiguation MCP Server

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

Annotations provide readOnlyHint=true and openWorldHint=true, indicating safe, open-ended operations. The description adds valuable behavioral context beyond this: it explains filtering logic (excludes candidates with no institutional affiliation), ranking behavior (institution-aware ranking with context), and performance details (higher default limit, detailed logging). This enhances understanding without contradicting 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 is front-loaded with the core purpose but includes extensive sections (Args, Returns, Example usage, Enhanced Features) that, while informative, could be more streamlined. Some sentences, like 'AI can then select the best match or retrieve works for further verification,' are less essential. Overall, it's comprehensive but slightly verbose.

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 (5 parameters, 0% schema coverage, no enums, no nested objects) and the presence of an output schema (detailed in Returns section), the description is highly complete. It covers purpose, parameters, behavioral traits, usage examples, and enhanced features, providing all necessary context for an AI agent to invoke the tool effectively.

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 detailing all 5 parameters: 'name' (author name to search), 'context' (optional disambiguation context), 'limit' (max candidates with defaults), 'filter_no_institution' (exclusion logic), and 'enable_institution_ranking' (ranking toggle). It provides clear semantics, examples, and default values, adding significant value 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 tool performs 'enhanced autocomplete authors with intelligent filtering and ranking,' which is a specific verb+resource combination. It distinguishes from siblings like 'search_authors' by emphasizing disambiguation and filtering features. However, it doesn't explicitly contrast with all sibling tools like 'get_orcid_publications' or 'search_works'.

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 through examples ('Get high-quality candidates with institutional filtering') and notes that 'For seasoned researchers, institution hints and ranking help disambiguation,' suggesting context-aware scenarios. However, it lacks explicit guidance on when to use this tool versus alternatives like 'search_authors' or 'search_orcid_authors,' leaving some ambiguity.

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