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

Goes well beyond the annotations (readOnlyHint=false, idempotent) by disclosing runtime (~30 s per call), idempotent resume semantics with no double charge, the $0.50 research cap, coverage/limits reporting, namesake holding (linkedin_name_only), evidence-linking rules, and a prompt-injection warning. These are traits an agent cannot get from the 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?

Content is dense but delivered as one unbroken paragraph with long run-on sentences, so critical operational guidance (next_action.code, paid consent gate) is buried near the end. Little is wasteful, but the lack of structure hurts scannability.

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 10-parameter, no-output-schema, open-world tool this covers most of what's needed: cost, resume, limits, identity-linking confidence tiers, and failure/next-step codes. It stops short of describing the shape of the returned map (github vs linkedin vs coverage sections), which an agent must infer.

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 already 90%, but the description still adds semantics: max_cost_usd switches on the paid side, request_id is the resume key, github_orgs are 'checked, never trusted', and limits explain the coverage cuts. Minor gap: it doesn't tie limits fields to default behavior as the schema does.

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?

Opens with a specific verb+resource: it maps a company's public engineering ecosystem across named sources (official GitHub orgs via resolve_company, public members, repo contributors, GitHub company field, optional LinkedIn people). An agent can tell what it produces and how it differs from resolve_company and review_identity_match, which are named in context.

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 explicit conditional guidance: the paid LinkedIn side runs only with max_cost_usd and the user's consent, the same request_id resumes rather than re-bills, and the agent is told to branch on next_action.code. It never explicitly contrasts with the sibling get_company_map or find_people_behind_repos, so retrieval-vs-start selection is left to inference.

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