netintel_extract_resume

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

With no annotations supplied, the description carries the full behavioral burden; it does disclose the backing model (Claude Haiku) and the shape of the return value (clean JSON arrays with named fields), which is real added context. It omits input size limits, behavior on non-resume input, latency/cost implications, and error handling, so a mutation-free but model-backed call still has notable blind spots.

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?

One front-loaded sentence: verb, resource, output fields, then use cases. No filler. The trailing ellipsis suggests the text is truncated, which slightly weakens an otherwise tight structure.

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 and no annotations, the description does useful work by enumerating the returned fields, but it leaves gaps an agent needs: input limits, how non-resume text is handled, and any failure modes. Adequate, not complete, for a model-backed extraction tool.

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?

There is a single required parameter, 'text', with 0% schema description coverage, so the schema says nothing about its format or constraints. The description only hints that it should be resume/CV text; it doesn't state whether raw pasted text, file content, or a URL is expected, nor any length bounds.

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?

It names a specific verb (extract), a specific resource (resume/CV text), and the exact fields returned (name, contact info, skills, work experience, education). This cleanly separates it from the nearby generic siblings netintel_extract, netintel_extract_contact, netintel_extract_invoice and netintel_extract_table without the agent needing to open any schema.

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 downstream scenarios (applicant screening, candidate databases, recruiting), which implies when the tool is useful, but it never states when NOT to use it or which sibling to pick for non-resume text extraction. Usage is inferable rather than specified.

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