List observations
model_list_observationsList facts. Filter by parameterId, setId, evidenceStatus. Sets are a column (Base/Downside/Upside), not an axis.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| id | No | ||
| setId | No | ||
| parameterId | No | ||
| evidenceStatus | No |
model_list_observationsList facts. Filter by parameterId, setId, evidenceStatus. Sets are a column (Base/Downside/Upside), not an axis.
| Name | Required | Description | Default |
|---|---|---|---|
| id | No | ||
| setId | No | ||
| parameterId | No | ||
| evidenceStatus | No |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the behavioral disclosure burden. It discloses the non-obvious set-as-column behavior and the filtering capabilities, but does not mention return shape, ordering, pagination, or error behavior. 'List facts' implies a read-only operation, which provides minimal transparency.
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 short sentences with the core action front-loaded and no filler. The filter note and the set-column clarification are both high-value and earn their 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?
Adequate for a simple list operation but not fully complete: with no output schema, the return format is unspecified; the id filter is undocumented even in the description; and 'facts' is vague. Still, the essential list/filter/set semantics are present.
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 maps parameterId, setId, and evidenceStatus to filter semantics and adds meaning to setId via the column clarification. However, the id parameter is not mentioned, and evidenceStatus values/format are not described.
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?
States the action ('List') and the resource ('facts', i.e. observations), and names the supported filters. It is distinguishable from sibling list tools like model_list_parameters and model_list_edges by the resource type, though 'facts' is informal and does not precisely define what an observation is.
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?
Gives clear filter context and disambiguates the setId semantics with 'Sets are a column, not an axis', which prevents a common misuse. No explicit alternatives or when-not-to-use guidance, but for a list tool with obvious siblings, the filter guidance is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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