rostra

record_decision_outcome

Log an observed outcome for an existing decision, storing status, measurements, and notes to provide evidence-backed tracking and enable Decision CI.

Instructions

Append an observed decision outcome

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "working_directory": "<working_directory>",
      "decision_id": "<decision_id>",
      "status": "<status>",
      "observed_at": "<observed_at>",
      "measurements": "<measurements>"
    },
    "name": "record_decision_outcome"
  }
}

Input Schema

Name	Required	Description	Default
`notes`	No
`status`	Yes
`decision_id`	Yes
`observed_at`	Yes
`measurements`	Yes
`working_directory`	Yes
`superseding_decision_id`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0-beta.2Sep 2, 2026

TDQS

C2.6/5.0

Behavior2/5

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

No annotations are provided, so the description must carry the behavioral disclosure burden. 'Append' conveys an additive, non-overwriting write, but it doesn't mention side effects, whether a prior decision must exist, validation behavior, or idempotency. This is thin for a mutating tool.

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

Conciseness3/5

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

The phrase is compact, front-loaded, and free of fluff. However, it is under-specified as a definition for a 7-parameter tool with nested objects; brevity is achieved by omitting useful context rather than packing it efficiently.

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

Completeness2/5

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

The schema and output schema carry much of the structured detail, so restating return values isn't necessary. Still, the tool sits within a deliberation lifecycle with no mention of when outcome recording happens, whether it depends on a prior decision, or how it relates to review_decision_change. An agent must infer the operative context.

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

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description doesn't reference any parameter. The schema itself provides rich constraints (required fields, status enum, UUID/date formats), but the description adds no meaning to decision_id, status, observed_at, measurements, or working_directory.

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?

The description names a specific action ('Append') and object ('observed decision outcome'), so the tool's core purpose is clear. It doesn't explicitly differentiate from siblings like review_decision_change or query_decisions, but 'append' implies a distinct write operation.

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 guidance about when to use this tool, what prerequisites exist, or when to prefer a sibling such as review_decision_change or query_decisions. The only context is implied by the name and schema.

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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