glycan_lookup

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

There are no annotations, so the description carries the full burden. It discloses the core read-only nature ('look up') and the key returned fields. However, it does not mention what happens with invalid/unknown accessions, whether the accession format is validated, or any source/version details. This is adequate but not rich.

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 English purpose and is not overly long, but it repeats the same information in Chinese, which adds redundancy for an AI agent. The structure is a single run-on sentence rather than cleanly separated clauses.

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?

For a single-parameter lookup tool with an output schema, the description covers purpose, input format, and returned fields. It omits error behavior, identifier normalization, and relationship to similar glycan-related tools, leaving some gaps in operational context.

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 input schema provides only a parameter name with 0% description coverage. The description compensates by explaining that the parameter is a GlyTouCan accession and gives a concrete example ('G00051MO'). It does not provide a full pattern or edge-case guidance, but the example makes the parameter usable.

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 states a specific verb ('look up'), a specific resource ('glycan structure by GlyTouCan accession'), and enumerates the returned data (composition, mass, monosaccharide count, sequence info). This clearly distinguishes it from sibling lookup tools like lipid_lookup or protein_glycosylation.

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 makes the intended context explicit: look up a glycan by its GlyTouCan accession, for glycomics research. It does not explicitly discuss when not to use this tool or mention alternative tools, but the input requirement and purpose are clear enough for an agent to select it appropriately.

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