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

Beyond the readOnlyHint annotation, it discloses that the tool never applies edits, never averages resumeQuality with jobFit/keywordCoverage, never invents figures/names/tools, and returns confirm_with_user and next_step. It also documents the AI-plus-rule-based fallback path and what ai.message reports when the model is unavailable, which is exactly the kind of behavior an agent cannot 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?

The trigger is front-loaded and every clause carries information, but the middle is a dense run-on ('a probable qualitative outcome is a suggestion to keep only if true' is hard to parse) and naming the exact models and hosting platform is implementation detail that does not help tool selection.

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 no output schema, the description carries the full return-value burden and does so: it enumerates the headline metric, the two separate scores, missing posting terms, per-edit borrowed terms, confirm_with_user, and next_step. Combined with the guard and fallback behavior, an agent knows what comes back and how to present it.

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 both parameters are fully described in the schema (including the 120–20,000 character bound and the fetch_job_post handoff for links). The description adds no parameter-level meaning, so the baseline 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?

The first sentence names a specific verb and resource ('resume rewritten or tailored') plus the trigger condition ('for a specific job posting'), so the agent can distinguish it from check_resume or match_job. It stops short of explicitly naming which sibling to use instead when the user only wants scoring versus rewriting.

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?

It gives a clear activation condition ('when the user wants their resume rewritten or tailored for a specific job posting'), which is stronger than most definitions. It does not state when NOT to use it or name alternatives like match_job/keyword_scan for scoring-only requests, so the routing burden is only partly removed.

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