convert_identifiers

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

The description goes well beyond the annotations (readOnly, idempotent, non-destructive) by disclosing critical behaviors: automatic prefixing of bare IDs ('1956' -> 'hsa:1956'), the 10-entry per-request limit with truncation, the full-database conversion when entry_ids is None with a size warning (~700 KB for hsa↔ncbi-geneid), and the rejection of cross-kind pairs. This adds substantial transparency that annotations alone do not provide.

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 well-structured: an opening purpose statement, then a paragraph of key constraints, followed by bullet-point-style parameter details. Every sentence adds unique value, and the information density is high without redundancy. It is appropriately sized for the complexity of the tool.

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 all necessary context: purpose, allowed conversions, parameter behavior, size implications, and explicit rejections. Since an output schema exists, it does not need to describe return values. The description is complete for an agent to invoke the tool correctly, including edge cases and performance considerations.

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?

With schema description coverage at 0% and no enums, the description carries the full burden of explaining parameters. It thoroughly describes source_db and target_db with valid values (hsa, T01001, ncbi-geneid, etc.), and entry_ids with its optional nature, auto-prefix behavior, 10-item limit, and None meaning full database. This is far more informative than the bare schema definitions.

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 the tool's purpose: 'Convert KEGG IDs to/from external database identifiers.' It then specifies exact conversion kinds and pairs (genes ↔ ncbi/uniprot, chemistry ↔ pubchem/chebi) and rejects cross-kind conversions. This distinguishes it from sibling tools like get_gene_info or search_genes, which retrieve information rather than convert identifiers.

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 provides clear context for when to use the tool (converting IDs between KEGG and external databases) and explicit constraints (only within one kind, 'kegg' not allowed, cross-kind rejected). However, it does not explicitly mention alternative tools or when not to use it in favor of other operations, so it falls short of the highest score.

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