Observe your self_classification distribution across your branch-1 decisions.
| Name | Required | Description | Default |
|---|---|---|---|
| auth_token | No | Your DA agent auth token. Optional when this connection already carries one (Authorization: Bearer header on the remote server, or DA_AUTH_TOKEN for a local stdio server); an explicit value takes precedence. |
Changes observed during successful MCP inspections.
Input schema / properties / auth_token / descriptionPrevious value: -"Your DA agent auth token"New value: +"Your DA agent auth token. Optional when this connection already carries one (Authorization: Bearer header on the remote server, or DA_AUTH_TOKEN for a local stdio server); an explicit value takes precedence."Input schema / requiredRemoved value: -[
- "auth_token"
-]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Observe' weakly implies a read-only operation, but the description does not explicitly state side effects, data freshness, auth requirements, or what form the distribution takes. The tool name already includes 'get', so the description adds little behavioral insight beyond a generic read intent.
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?
A single, front-loaded sentence that contains no filler. Every word contributes to identifying the object and scope of the operation.
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 tool is simple, with one optional parameter and no output schema, so the description is close to sufficient. Still, with no annotations and no explanation of what 'branch-1 decisions' or the returned distribution looks like, an agent may need additional context to fully understand the result or the intended use case.
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 only parameter, auth_token, is fully described in the schema with 100% coverage, including precedence behavior. The description adds no parameter-specific meaning, but it does not need to because the schema already documents the parameter adequately.
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 states a specific verb ('Observe') and resource ('self_classification distribution across your branch-1 decisions'), making the tool's purpose understandable. It does not explicitly differentiate from sibling tools like get_decision_metadata_distribution, so it misses full sibling distinction, but the scope is clear enough.
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 want to see your self_classification distribution over branch-1 decisions. However, it offers no explicit exclusions or alternatives, and it does not clarify how this differs from related distribution/observation tools.
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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