evidence_listList evidence records for a specific project, with optional filtering by evidence type, to retrieve auditable proof of business operations.
列出指定项目的证据，可按证据类型筛选。
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| project_id | Yes | ||
| evidence_type | No |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds that results are scoped to a project and can be filtered by type, which is useful, but it does not disclose pagination, ordering, limit behavior, or return shape. This is modest added context beyond the annotations, with no contradiction.
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 a single sentence that front-loads the action and scope and appends the optional filter. There is no filler, repetition, or unnecessary detail; every word 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?
For a simple read-only list tool with strong annotations, the description is nearly sufficient: it identifies the required project scope and the optional filter. However, there is no output schema, no mention of the limit parameter, and no guidance separating it from knowledge_search, so an agent has minor gaps to resolve for fully optimal 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 0%, so the description must compensate. It clarifies project_id ('指定项目') and evidence_type ('按证据类型筛选'), but it says nothing about limit or its pagination semantics. The compensation is partial: two of three parameters get some human-readable meaning, but one remains undocumented.
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 uses a specific verb ('列出' / list), a clear resource ('证据' / evidence), and a scope ('指定项目' / specified project), and it adds the filtering dimension by evidence type. It clearly states what the tool does, but it does not explicitly differentiate itself from siblings such as evidence_add or knowledge_search, so it misses the top sibling-distinction bar.
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 phrasing implies the tool is for enumerating a project's evidence and supports an optional type filter, which provides a usable context. However, there are no explicit when-to-use, when-not-to-use, or alternative tool recommendations, leaving the agent to infer the choice against siblings like evidence_add and knowledge_search.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.