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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds behavioral context by listing the three output components (market rate, brand index, displacement signals) and providing a concrete example of the output format, which helps the agent understand what the tool returns beyond the schema.

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 three sentences with an illustrative example. It is not overly verbose, but the example is a bit extended. All sentences contribute meaningful information about purpose, usage, and expected output, earning a high score without quite reaching perfect conciseness.

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?

The tool has one parameter, no output schema, and annotations cover safety. The description explains what it does, when to use it, and what it returns (with an example). It is complete for the tool's simplicity, though it could theoretically clarify return structure or limitations, but that's not necessary for this level of complexity.

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 0%, so the description must compensate. It does so by mentioning 'any vendor' and using Salesforce as an example, clarifying that vendor_name refers to the vendor being analyzed. Though it doesn't explicitly name the parameter, the single required parameter is obvious from context, making this sufficient.

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 the tool's verb (returns), resource (market rate data, brand index snapshot, competitive displacement signals), and purpose (vendor diligence for PE/VC portfolio companies). It distinguishes itself from siblings by combining multiple intelligence types into one composite output, as evidenced by the specific data categories listed.

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 provides explicit context for when to use: 'when conducting vendor diligence for a PE or VC portfolio company before a value creation initiative.' It does not explicitly name alternatives or exclusions, but the use case is clear and specific enough to guide selection among sibling tools.

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