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dm_decide

Request a decision for an agent workflow and get back a choice, confidence, allowed options, and next-step instruction, with uncertain cases routed to a hosted model.

Instructions

Ask for a decision. Input: {point_id*, context*, [teacher_cost]}. Returns {decision_id, mode, choice, allowed_options, probabilities, confidence, threshold, instruction}. mode teacher: decide yourself, then do_record. mode auto/rule: use choice.

Input Schema

NameRequiredDescriptionDefault
payloadYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
Behavior3/5

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

No annotations are provided, so the description must carry the behavioral burden. It discloses mode semantics and the expected follow-up action (do_record in teacher mode), but it does not state whether the call mutates state, what permissions are required, or any rate limits.

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

Conciseness4/5

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

The description is compact and front-loaded, moving from purpose to input, return, and mode behavior without filler. The bracket notation is efficient but slightly cryptic, which keeps it from being maximally clear.

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?

Because an output schema exists, the description need not fully explain return values, and it does provide the key mode semantics. However, the input schema is nearly empty and the description does not fully explain the nested payload fields or the safety profile, so an agent still lacks important context for correct invocation.

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 schema describes only an opaque payload object with 0% property-level coverage, so the description must compensate. It usefully enumerates expected fields as point_id*, context*, and optional teacher_cost, but it does not explain their meaning, types, or formats, leaving significant semantic gaps.

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

Purpose3/5

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

"Ask for a decision" states a verb and resource, but the purpose remains abstract: it does not say what kind of decision, in what domain, or how it differs from sibling tools such as do_decide. The mode and return details add some clarity, but the core purpose is still vague.

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

Usage Guidelines3/5

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

The description gives mode-specific handling: in teacher mode decide yourself and then do_record, while in auto/rule mode use the returned choice. It does not explain when to call dm_decide instead of alternatives like do_decide, nor does it state prerequisites or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.