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

No annotations are provided, so the description carries the full burden. It discloses that evidence outranks generated ICP simulation, and includes a detailed approval note explaining tiers (first use may require approval, from-now-on vs just-once approval). While it doesn't discuss idempotency or error handling, the information about write-tier and approval behavior adds significant value 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 concise and well-structured. The first sentence states the core purpose. The second sentence clarifies priority over ICP simulation. The third sentence provides usage guidelines. The bracketed approval note is separate and informative. No unnecessary words or repetition.

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?

Given the tool's complexity (11 parameters, no output schema), the description covers the essential aspects: what the tool does, when to use it, and important behavioral notes (approval, priority over ICP). It lacks explicit mention of return values (e.g., whether it returns the stored evidence ID) but that is common for write tools. The description adequately prepares the agent for correct invocation.

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 baseline is 3. The description adds context for the 'class' parameter (determines rank weight) and provides examples for 'source' (e.g., 'support ticket #…'). The approval note explains the 'may_open_loop' and 'may_refine_icp' parameters implicitly. While the schema already describes each parameter, the description reinforces their meanings and adds behavioral nuances, elevating it above baseline.

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 purpose: 'Store one piece of REAL Customer Evidence for this company'. It enumerates specific types of evidence (paying-customer words/behavior, telemetry, review, etc.) and distinguishes it from ICP simulation, which is handled by a sibling tool (create_icp). This provides a specific verb-resource combination and differentiates from similar tools.

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 explicitly specifies when to use the tool: 'Use when the operator pastes a real customer quote, a call note, a review, or a provenanced usage signal — NOT for inventing personas (use Customer Hunter / create_icp for hypotheses).' This provides clear context and explicitly excludes alternative scenarios, offering direct guidance on tool selection.

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