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

With no annotations provided, the description carries the full behavioral burden, and it succeeds: it discloses that the tool returns ranked matches, confidence levels (high/medium/low), evidence snippets, and a ready-to-use array. It also reveals conservative matching behavior on ambiguous short tokens and the preference for multi-mention or canonical-name matches, which helps an agent calibrate expectations.

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 description is four sentences with no filler. It front-loads the core purpose and example inputs, then covers output shape and tie-breaking behavior. Every sentence adds value.

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, read-only-style inference tool with no output schema, the description is complete: it explains inputs, outputs, confidence levels, evidence snippets, and how the result can be chained into sibling tools. An agent has enough to decide when to call it and what to expect in return.

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?

The single parameter text is fully described in the schema (100% coverage), including type, min/max length, and examples. The tool description adds examples like careers page and DevTools network tab, but these are incremental rather than essential; the schema already does most of the work. A baseline of 3 is appropriate because the description does not meaningfully change parameter understanding.

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 description states a specific verb and resource: 'Infer a GTM stack from a freeform text blob'. It lists concrete input examples (careers page, job posting, HTML, RFP, etc.), and distinguishes the tool from likely siblings by describing its output as ranked tool matches suitable for chaining into scan_stack or find_overlaps. This gives an agent a clear mental model of what the tool does and how it differs from related tools.

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 explicit usage triggers: 'Use when the user says “I don’t know what we use” or pastes a competitor’s careers page to scout.' It also indicates downstream tools (scan_stack, find_overlaps) for chaining. It does not explicitly discuss when not to use it or name an alternative tool to prefer, so it misses the full when/when-not/alternatives bar.

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