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

Annotations already cover read-only safety, and the description adds meaningful behavioral context: meta entries are traced claims, not established facts, and should not be taken on faith. This provenance caveat is genuinely useful beyond the structured annotations, though it stops short of describing ordering, pagination, or output shape.

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 dense sentences with no wasted words. It front-loads what the tool returns, then adds the essential trust-and-trace guidance that an agent needs before calling it.

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 simple read-only tool with two optional parameters, the description covers the nature of the data, its provenance model, and the follow-up route to read_facts. The lack of an output schema is partially mitigated by explaining that rows name their source rows, though exact response fields are not described.

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 100%, so the description does not need to repeat parameter details. The category list in the description does reinforce the 'type' enum, and the limit parameter is already fully documented in the schema. No additional parameter semantics are provided, so baseline 3 is appropriate.

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 names a specific verb-resource pairing: reading meta entries—patterns, anomalies, insights, and warnings—recorded by agents about the swarm. It clearly distinguishes itself from read_facts by explaining that meta rows are claims with trails, not standalone findings.

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 tells the agent when read_meta is appropriate and how to use it correctly: treat rows as claims with provenance, and follow derived_from into read_facts before relying on them. This names the key sibling alternative and gives the condition for switching to it.

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