lipid_lookup

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

With no annotations, the description carries the burden of behavior, and it discloses the lookup action and the returned fields (name/formula/SMILES/InChIKey/DB cross-refs). It does not mention behaviors such as exact-match requirement, case sensitivity, availability, rate limits, or error handling, but for a simple read lookup the core behavior is reasonably transparent.

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 compact and front-loaded, with the key action in the first phrase. The bilingual repetition adds length but is purposeful for the apparent Chinese-speaking user base; there is no filler.

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 with an output schema, the description covers what the tool does, what to provide, and what comes back (name/formula/SMILES/InChIKey/DB cross-refs). Minor omissions, such as exact matching behavior or acceptable LM ID formats beyond two examples, keep it from a 5.

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 schema's lm_id parameter has no description (0% coverage), so the description is the only source of semantic meaning. It provides the identifier type (LM ID), concrete examples (LMFA01030001 arachidonic acid, LMGP01010001 phosphatidylcholine), and states the expected input pattern, which adequately compensates for the empty 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 names a specific operation ('Look up LIPID MAPS lipid structure'), the resource (LIPID MAPS), and the key identifier (LM ID), and enumerates returned fields (name/formula/SMILES/InChIKey/DB cross-refs). This is specific enough to distinguish it from sibling compound/metabolite tools like compound_info or chebi_compound.

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 use case is implied through the examples and '用于脂质组学研究' (for lipidomics research), but no explicit guidance says when to prefer this tool over alternative compound/glycan/metabolite lookups. It gives clear context but no exclusions or alternative routing.

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