Pubmed Spell Check

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

No annotations are provided, so the description carries the burden. It reveals the backing service (ESpell, an external NCBI call, hinting at network dependency), but says nothing about whether it returns corrections vs. suggestions, confidence levels, or failure behavior for non-biomedical input.

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 short sentences, front-loaded with the action, with no filler or redundancy. Every clause earns its place.

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?

An output schema exists, so return values need not be documented. For a one-parameter read-style lookup the description covers the essentials, though it could note input expectations or that it is non-mutating given the absence of annotations.

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 0% for the single 'query' parameter. The description constrains the input as a 'biomedical query', which adds real meaning beyond the bare 'string' type, but gives no guidance on length, syntax, or how malformed queries are handled.

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?

States a specific verb and resource ('Spell-check a biomedical query') and names the underlying service (NCBI ESpell). It is clearly distinguishable from siblings like pubmed_search or pubmed_lookup_mesh, though it never explicitly frames itself against them.

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?

'Useful for query refinement before searching' implies a workflow position but is soft guidance ('useful for') rather than an explicit when-to-use rule. No alternatives or exclusions are named, so the agent must infer the routing.

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