Load once at conversation start to get the standing brief on reading results and measured findings that make numbers interpretable.
The standing brief: how to read results, and the measured findings that make a number interpretable. Load this ONCE at the start of a conversation.
Stable across runs — refetch only if told the spec changed.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden, and it usefully discloses that the content is stable across runs and should be cached unless the spec changes. It does not explicitly discuss read-only behavior or side effects, but the load-once guidance provides strong operational context.
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?
Three short sentences front-load the purpose, usage timing, and caching rule with no filler. Every sentence adds useful guidance.
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 output schema presumably explains the returned brief, so the description need not enumerate return values. It covers purpose, usage timing, and stability, though it could be slightly more explicit about what kinds of measured findings are included.
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 tool has zero parameters, so there are no parameter semantics to explain beyond the baseline. The description appropriately avoids inventing parameter details.
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 positions this as a contextual primer rather than a metric-computation sibling: it provides a standing brief for interpreting results. It does not specify exactly what form the brief takes, but the distinction from the many metric tools is clear.
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?
It explicitly says to load it ONCE at the start of a conversation and to refetch only if told the spec changed. That gives both the when and the when-not, leaving little inference for the agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.