recommend_subgraph

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes key behaviors: automatic inference of domain/protocol type, inclusion of reliability scores and query URLs, and the need to replace [api-key] for live queries. However, it lacks details on error handling, rate limits, or authentication requirements, leaving gaps for a tool with no annotation coverage.

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 appropriately sized and front-loaded, with every sentence earning its place. It efficiently explains the tool's function, input processing, output components, and usage instruction without redundancy or unnecessary elaboration.

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?

Given no annotations and no output schema, the description provides a solid foundation but has gaps. It covers the tool's purpose, input interpretation, and output structure, but lacks details on error cases, performance characteristics, or exact return format. For a recommendation tool with 2 parameters and no structured output documentation, this is adequate but not fully comprehensive.

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 description coverage is 100%, so the schema already documents both parameters (goal and chain). The description adds marginal value by emphasizing the natural-language aspect of 'goal' and mentioning optional chain filtering, but does not provide additional syntax or format details beyond what the schema specifies. Baseline 3 is appropriate when schema does the heavy lifting.

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 with specific verbs ('returns the best matching subgraphs') and resources ('subgraphs with reliability scores and query URLs'). It distinguishes from siblings by focusing on recommendation based on natural-language goals rather than detailed lookup (get_subgraph_detail), statistical listing (list_registry_stats), or general search (search_subgraphs).

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 this tool ('Given a natural-language goal...'), but does not explicitly state when not to use it or name specific alternatives among the sibling tools. It implies usage for goal-based matching rather than direct queries or searches, offering good guidance without exclusions.

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