compute_similarity

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

Annotations declare readOnlyHint=true, and the description adds that all scoring is algorithmic and provides details on outputs (fit tier, gap analysis, recommendations). No contradictions; the description enhances transparency beyond annotations.

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 sentences, no unnecessary words, front-loaded with the most important information (what it does, how it scores, outputs). Highly efficient.

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?

Despite no output schema, the description fully explains return values (fit tier, per-skill gap analysis, recommendations). Low parameter count and high schema coverage leave no gaps. Complete for this tool's complexity.

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 covers both parameters (100% coverage) with concise descriptions. The description adds meaning by specifying the algorithmic methods and the output structure, which goes beyond schema basics.

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 clearly states it compares resume against job description using specific algorithmic methods (cosine similarity, Jaccard index, TF-IDF overlap, skill matching) and returns fit tier, gap analysis, and recommendations. It distinguishes itself from siblings by explicitly noting 'No AI calls — all scoring is algorithmic.'

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 implies when to use (algorithmic comparison) and contrasts with AI-based alternatives by stating 'No AI calls.' It does not explicitly list when not to use or name alternatives, but the context is clear enough for an agent.

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