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

Even though annotations already mark the tool readOnly, idempotent, and non-destructive, the description adds crucial behavioral context: null means 'not researched' for key fields, specific sparsity percentages with measurement dates, LinkedIn fields being null corpus-wide, and person-shaped data never being sent. This goes well beyond what annotations provide.

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 long but every sentence adds operational value: auth requirements, sparsity caveats, data absence caveats, endpoint boundary, and sibling-tool disambiguation. There is no filler or repetition of the schema, and the most decision-critical warning about sparse fields is front-loaded.

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 paid read-only look-up with six fully documented parameters, an output schema, and helpful annotations, the description covers all remaining contextual gaps: auth level, null interpretation, population scope, search behavior, and what the endpoint will not 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?

Schema description coverage is 100% and each parameter already has detailed semantics including types, defaults, enums, constraints, and cursor semantics. The description reinforces the correct interpretation of missing values, but does not need to add per-parameter detail; baseline 3 is appropriate because the schema carries the burden.

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 clearly states what the tool returns: a paid firm intelligence profile with explicit data content beyond SAP practice size, partner level, delivery flags, day rate, clients, and LinkedIn URL. It also differentiates itself from search_firms by noting the public directory covers a different, wider population, so the agent can distinguish it from siblings.

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?

It gives explicit when-to-use guidance with 'search by name', licensing prerequisites ('requires a subscriber API key, Legend tier or above'), an exclusion ('person-shaped fields are NEVER served by this endpoint at any tier'), and a routing cue toward the alternative ('the public directory search_firms is a different, wider population').

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