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Get Self-Classification Distribution

get_self_classification_distribution
Read-only

Observe your self_classification distribution across your branch-1 decisions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
auth_tokenNoYour 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.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / auth_token / description
      Previous 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."
    • removedInput schema / required
      Removed value: -[
      -  "auth_token"
      -]
  2. Added

TDQS

B3.3/5.0
Behavior3/5

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

Annotations already establish readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds only the scoping detail that the distribution spans the agent's own branch-1 decisions; it says nothing about the shape, granularity, or freshness of the returned distribution, which would be the useful added context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single short sentence with zero filler, and the object of the operation is front-loaded. Nothing can be trimmed without losing information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description should give the agent some sense of what a 'distribution' return looks like and what 'branch-1 decisions' scope means, and it does not. For a read-only tool with one optional parameter this is adequate but leaves a real gap around return expectations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

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, including its optionality and precedence rules, so the schema already does all the work. The description contributes no parameter meaning beyond that, which is the expected baseline for 100% schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ('Observe') and resource ('your self_classification distribution') with an explicit scope of 'branch-1 decisions.' However, it does not distinguish itself from the similarly shaped sibling get_decision_metadata_distribution, and the terms 'self_classification' and 'branch-1 decisions' are domain jargon an agent cannot resolve from the description alone.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use guidance, no statement of prerequisites, and no mention of alternatives such as get_decision_metadata_distribution or list_classifications. The agent is left to infer that this is a read/reporting call from annotations alone.

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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