linkfetch_search_people

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

With no annotations, the description carries the full behavioral burden and does well: it discloses cache-first lookup, live account execution in Cloud mode, safety limits, and the linkedin_not_connected / extension_required error outcomes. It omits return-shape details and leaves 'safety limits' unquantified.

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?

Two sentences, both front-loaded and dense with useful information. No filler or redundancy; the scoping and fallback behavior are stated before the error cases.

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 tool with moderately complex cache/live/auth behavior, 100% parameter coverage, and no output schema, the description covers the behavioral essentials well. The main gap is that it does not describe the response shape, which the absent output schema would otherwise require.

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 description coverage is 100%, so the baseline is 3. The description adds only 'by keyword' (mapping to q) and provides no syntax, format, or exclusion details for q, limit, or offset beyond what the schema already documents.

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?

States a specific verb+resource ('Search LinkedIn people by keyword') and distinguishes itself from siblings like search_companies, search_jobs, and get_profile. The 'like the LinkedIn search bar' analogy makes the retrieval model immediately clear.

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 clear operational context and prerequisites: cache-first behavior, live fallback only in Cloud mode, and specific error responses otherwise. It does not name alternative sibling tools or explicitly say when not to use it, so it falls short of the top score.

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