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

With no annotations, the description carries the full burden, and it does disclose meaningful behavior: output is a per-requirement fit report including both supporting evidence and gaps, and 'cites, never asserts' signals evidence grounding rather than generative claims. It does not explicitly confirm the operation is read-only or discuss limits on requirement volume, which keeps it short of a 5.

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?

Two sentences, zero waste: the first front-loads the input-to-output contract, the second names the guarantees (evidence plus gaps, citations over assertions). Nothing could be removed without losing a distinct commitment.

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?

For a one-parameter, annotation-free tool with no output schema, the description covers input shape, output structure, and evidentiary stance, which is close to complete. Minor gaps remain around whether the report covers all requirements when some cannot be evidenced and how gaps are presented.

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% and the single parameter already documents that it accepts a job description or requirements one per line. The description reinforces per-requirement granularity through 'for each requirement,' but adds no formatting, size, or syntax detail beyond the schema, so the baseline of 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 concrete input (job description or requirement list) and a concrete output (an evidence-backed fit report mapping each requirement to pages and figures, plus gaps). This is distinguishable from siblings like list_capabilities or search_portfolio, though those siblings are never named, so the differentiation is inferred rather than explicit.

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 phrase 'Given a job description or a list of requirements' implies the usage context, which is adequate for a single-purpose matching tool. However, there is no explicit when-to-use vs. when-not guidance and no mention of alternatives such as search_portfolio for ad-hoc queries, leaving the choice to inference.

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