pubmed-mcp-server

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

Annotations declare readOnlyHint=true and openWorldHint=true. The description adds value by specifying the external resource contacted ('NCBI'), which explains the openWorldHint. However, it omits details about what happens when no corrections are found or rate limiting considerations.

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?

Two sentences totaling fewer than 20 words. The first sentence front-loads the core functionality, and the second provides usage context. No redundant or filler text.

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 simplicity (single string parameter), read-only nature (annotations), and existence of an output schema, the description adequately covers the essential operational context. It could be improved by mentioning edge cases (e.g., correct queries return empty suggestions).

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 100% schema description coverage, the schema already fully documents the 'query' parameter ('PubMed search query to spell-check'). The description references 'a query' but adds no additional semantic details, syntax guidance, or examples beyond the schema baseline.

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 ('Spell-check a query') and the expected output ('get NCBI's suggested correction'), distinguishing it from sibling tools like pubmed_search_articles or pubmed_fetch_articles which handle retrieval rather than query validation.

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?

Provides clear context for when to use the tool ('Useful for refining search queries'), indicating it should be used as a preprocessing step. However, it lacks explicit negative constraints (when not to use) or named sibling alternatives for different query correction strategies.

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