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

Annotations already establish readOnly/idempotent/non-destructive, and the description adds substantial context beyond them: contact details removed first, nothing stored, deterministic output ('the same result for the same text'), English findings regardless of the lang field, and that resume_outline/job_requirements lines not matching verbatim are dropped and counted. That last rule and the PII stripping are behavior an agent could not infer from structured fields.

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?

Front-loaded with the purpose and trigger, then a single 'Returns:' block, then optional params, then fixed rules — a sensible ordering, and every element carries information. It is a dense, long paragraph with repeated 'Optional:' lead-ins and semicolon-chained clauses that make it slower to scan than it needs to be.

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?

With 9 parameters, no output schema, and a nested ResumeOutline type, the description carries the necessary load: it enumerates the three output groups, explains the question-id linkage to check_interview_answer, states language and PII handling, and documents what the optional inputs change. Nothing an agent needs to invoke it correctly is missing.

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%, so baseline is 3, but the description adds effect-level meaning the schema lacks: company yields questions from what large US employers publish with source links, and interview_date yields days-left plus practice prioritization. It also explains the resume_outline/job_requirements verbatim-match constraint and its drop-and-count consequence, which is more precise than the schema wording.

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 specific verb and artifact ('builds a job interview prep set') and scopes the target population (PM, program manager, TPM, PMO lead, business analyst) plus the inputs (resume text, optional posting). It also hands off to the sibling that consumes its output ('each question has an id to pass to check_interview_answer'), so it is clearly distinguishable from build_story_bank or start_mock_interview.

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?

Gives an explicit trigger: 'Use it when someone shares a resume (pasted or attached) before an interview, or asks what a hiring manager will ask them.' That covers the when. It does not name the nearest alternatives (prepare_interview_plan, match_resume_to_job, check_interview_answer) as when-not paths, so the agent must infer the boundary from sibling names alone.

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