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

With no annotations provided, the description carries the burden of behavioral disclosure. It does a solid job by explicitly describing the pagination contract: byte-budgeted chunks, the `complete` flag, `cursor`, and `next_cursor`, including the terminal page condition. It also conveys that this is a read-oriented reporting operation, though it stops short of detailing auth or exact response format.

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 are used efficiently: the first defines what the tool returns, and the second explains pagination. The 'INFLUENCE' label and 'market report' phrase add color but are not wasteful. The pagination rule is placed after the core purpose, which is good.

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 paginated cohort-report tool with no output schema and no annotations, the description covers the main operational contract: input is a cohort plus optional cursor, output is artifact counts plus agent-readiness dimensions, and large results require following the cursor loop. It could be more explicit about the required kind/slug usage and exact response structure, but the description is sufficient for a competent agent to invoke correctly.

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 50%, covering `cursor` and `context`, while `kind` and `slug` only have names and an enum. The description clarifies the output and pagination semantics and implicitly relates the result to a cohort identified by kind and slug, but it does not add meaningful detail about how those required parameters should be selected or combined.

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 states a specific purpose: returning artifact counts by type and agent-readiness dimensions for every member of a cohort. The phrase 'capability matrix behind a market report' clearly differentiates this from sibling tools like cohort_stats or cohort_rankings, which focus on different aggregates.

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 gives clear context for when to use the tool: to obtain the capability matrix for a cohort. It does not explicitly name alternatives or state when not to use it, but the intended use case is clear enough for an agent to route correctly among the siblings.

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