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

Beyond annotations, the description discloses meaningful behavior: BFS traversal from a seed, hop distances on nodes, and frequency-based edge weights. This clarifies the tool's internal algorithm and output semantics, which is substantial context beyond the read-only and idempotent hints.

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 short sentences with no filler: the core algorithm is front-loaded, return contents are stated, and use cases follow. Every sentence earns its place.

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?

There is no output schema, but the description covers the essential return shape (nodes/edges with hops and weights). The main completeness gap is parameter semantics and the absence of any note about result-size constraints or error behavior, but for a read-only graph query the description is largely sufficient.

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 0%, so the description must compensate, but it does not explain the three parameters. It refers to a 'seed' conceptually without linking it to the 'skill' parameter, and it never clarifies how max_hops or max_nodes affect traversal and results. The schema's names and constraints partially carry meaning, but the description adds little parameter-level value.

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?

Description names a specific resource ('multi-hop related-skill graph'), a concrete algorithm ('BFS from seed'), and the returned components ('nodes with hops_from_seed and edges with frequency-based weights'). This is distinct from sibling tools like jobs_related_skills, making the tool's purpose immediately identifiable.

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 gives three explicit use cases: broadening candidate filters, curriculum design, and job-description templating. It does not explicitly state when not to use it or name a sibling alternative, but the application context is clear enough for an agent to decide when this tool fits.

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