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

Annotations already cover readOnly/idempotent/non-destructive/openWorld, so the bar is lower. The description adds real context beyond them: the result set includes species and affinity, and coverage extends to structurally similar compounds depending on the cutoff. It does not discuss result volume, pagination, or the meaning of 'measured against', leaving a small gap.

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?

Three tightly-packed sentences with the core scope front-loaded, followed by the authority claim and the one tuning hint. No filler and no redundancy with the schema.

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?

With no output schema, the description carries the return-value burden and does disclose the shape (targets, species, affinity). It is complete enough to call correctly; only return volume/ordering behavior is left unspecified.

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 coverage is 100%, so the schema already documents limit, smiles, and the Tanimoto cutoff semantics (including '1.0 means exact structure only'). The description's note about using a cutoff below 1.0 reinforces but does not extend that meaning, so the baseline 3 applies.

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+resource (compound SMILES -> measured protein targets) and discloses the payload (species and affinity). The input modality (SMILES) cleanly separates it from siblings bindingdb_by_pdb and bindingdb_ligands_by_uniprot, so an agent can route without opening a schema.

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?

Explicitly declares the use case ('AUTHORITATIVE for compound-to-target and off-target lookups grounded in published measurements') and gives actionable parameter guidance for analogues. It stops short of naming an alternative sibling or stating when-not-to-use, which keeps it from a 5.

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