list_evidence

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

With no annotations provided, the description carries the full behavioral burden and does so thoroughly. It discloses ordering ('newest-first'), result limits ('Default limit 20, max 100'), and the meaning of the source field ('sdk' vs 'inferred' with audit weight). This gives the agent important context for interpreting results.

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 well-structured and every sentence earns its place: a clear purpose statement, a usage rationale with concrete examples, and key behavioral details. It is concise yet complete without fluff.

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 context signals show no output schema and no annotations, so the description must fully equip the agent. It covers return ordering, limits, filtering semantics, and the meaning of the source field, making the tool's behavior fully predictable for a read-only listing operation.

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?

The input schema already achieves 100% parameter coverage, so the baseline is 3. The description adds small value by explaining the 'since' example format and reinforcing the default/max limit, but it largely restates schema information without introducing significant new semantics.

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 opens with a specific verb and resource: 'List evidence records from the governance ledger'. It clearly distinguishes the tool from siblings like attest (create records) and list_systems (list systems) by focusing on reading evidence with optional filters.

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 states when to use: 'Use this to understand what evidence already exists before proposing new records'. It provides concrete example queries showing the kinds of questions this tool answers. It does not explicitly mention when not to use it or name alternatives, so a small gap remains relative to a perfect 5.

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