bc_get_alphafold_info_by_protein_symbol

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

With no annotations, the description carries full burden and effectively discloses the two-step workflow, return type (dict with URLs, scores, metadata, or error). It clearly describes the behavior beyond just 'get info' by detailing the conversion and prediction steps.

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 very concise: two sentences state purpose and process, followed by a structured returns block. No unnecessary words; front-loaded with the main action.

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?

The description covers purpose, process, and output structure. It mentions error handling ('or error message'). However, it could explicitly note that it requires network access to query an external database, but this is implied. Overall complete for a query tool with a clear output schema.

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%, providing descriptions for both parameters. The tool description does not add additional parameter semantics beyond what the schema already offers (e.g., specifying that protein_symbol is used for UniProt conversion). Baseline 3 is appropriate.

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 it queries AlphaFold using a protein name and explains the two-step process (converting symbol to UniProt ID, then fetching predictions). This distinguishes it from sibling tools like bc_get_uniprot_id_by_protein_symbol which only convert to UniProt ID.

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?

No guidance on when to use this tool vs alternatives like bc_get_uniprot_id_by_protein_symbol or bc_get_uniprot_protein_info. The description does not mention that this tool includes the UniProt lookup, so an agent might not know when to choose this over a simpler ID lookup.

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