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

Annotations already declare readOnly, openWorld, idempotent and non-destructive, so the safety bar is lower. The description still adds meaningful behavior beyond them: it discloses that nothing is read from LinkedIn and nothing is gathered about anyone, plus what the output actually contains (people plus ready LinkedIn/Google searches).

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?

Front-loaded with purpose and output categories, then the privacy note. It is fairly dense and stacks several quoted example phrasings, but each sentence carries information and nothing is padding.

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 read-only research tool with no output schema, the description covers what it does, what it returns, and its privacy stance. Only a slight gap remains around result format or limits, which is minor given the annotations.

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 schema already documents role, where, school and company. The description only loosely maps to these (hiring manager 'for the role', 'alumni of a school'), adding little syntax or format detail beyond the schema. Baseline 3 applies.

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 (finds people worth contacting at a company) and enumerates the categories returned: ad posters, recruiters, likely hiring managers, role incumbents, and school alumni. This clearly separates it from siblings like networking_plan or connection_score.

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?

Provides concrete example intents ('who should I contact at Monzo about data jobs?', 'find the hiring manager') that map directly onto when to invoke it. It stops short of naming an alternative sibling or stating exclusions, so it's clear context without explicit routing.

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