添加经营证据

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

With no helpful annotations (all hints false), the description carries the behavioral disclosure burden. It does add one meaningful behavior: evidence is only saved, and no automatic conclusion is formed. However, it does not explain side effects, immutability, duplicate handling, or what happens after saving, which matters for a write operation.

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 two short sentences with no filler. It front-loads the concrete evidence types and then adds the key behavioral caveat. Every sentence earns its place.

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 six parameters, one nested object, no output schema, and no annotations, the description is too thin to fully orient an agent. It leaves unclear the semantics of details, source_ref, and occurred_at (e.g., whether occurred_at is event time or creation time), and does not explain required fields or what the response will look like.

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 0%, so the description must compensate, but it only enumerates a subset of the evidence_type enum values (report, screenshot, platform_receipt, customer_feedback, observation) in Chinese. It adds no meaning for the required summary, project_id, or the optional details, source_ref, and occurred_at fields, leaving many parameters semantically unexplained.

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 names the verb '记录' (record) and the resource '经营证据' (business evidence), and it lists concrete examples like reports, screenshots, platform receipts, customer feedback, and observations. It stops short of explicitly naming a sibling tool to differentiate from, but the phrase '只保存证据，不自动下结论' does separate it from decision/conclusion-oriented 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 implies when to use the tool: when you need to preserve raw evidence without drawing conclusions. It does not explicitly state when not to use it or mention alternatives such as knowledge_ingest or decision_create, so the guidance is contextually suggestive rather than explicit.

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