Snapback

report_outcome

Report the outcome of an auto-applied fix (the self-heal interceptor calls this after it gate-applied a fix and retried). Pass failure_class, family, fix, confidence, action_class, and succeeded (did the retry work?). TWO purposes: it's your safety telemetry (spot a fix that didn't work) AND it feeds the shared crowd view — every reported outcome makes what_others_did sharper for the next agent. Token-scoped (so we know it's your org), free.

Input Schema

TableJSON Schema

Name	Required	Description
`fix`	No	the fix that was auto-applied
`family`	No
`fix_id`	No	the typed fix ID from the diagnosis (e.g. redis.maxmemory.4e24d4) — report the outcome against this so the crowd evidence is per-fix. Optional but recommended.
`succeeded`	Yes	did the single retry after the fix succeed?
`confidence`	No
`action_class`	No	retry \| refetch \| config
`failure_class`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 5, 2026

addedInput schema / properties / fix_id

Added value: +{
+  "description": "the typed fix ID from the diagnosis (e.g. redis.maxmemory.4e24d4) — report the outcome against this so the crowd evidence is per-fix. Optional but recommended.",
+  "type": "string"
+}

Changed1 schema field changedOct 2, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "description": "A JSON object (returned as text in result.content[0].text). Diagnosis tools return {matched, family, fix, confidence, source, action_class, auto_safe, gate}; other tools return their own JSON result.",
+  "type": "object"
+}

First observedSep 21, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already declare write/non-idempotent/open-world, and the description adds meaningful context beyond them: token-scoped identity ('so we know it's your org'), free of cost, and the fact that the data feeds a shared crowd view. It doesn't restate idempotency or return behavior, but the added auth/cost/crowd framing is genuine value.

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?

Front-loaded with the action, then the caller/trigger, then the rationale. The 'TWO purposes' sentence is a bit expansive but each clause carries information the agent cannot get from structured fields, so it largely earns its length.

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

Completeness4/5

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

With an output schema present, return values need not be described, and the description covers identity scoping, cost, trigger, and dual purpose. The main gap is not steering the agent toward fix_id for per-fix crowd evidence, which is the key behavioral nuance of this tool.

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?

Schema description coverage is 57%, and the description names the parameters to pass while clarifying 'succeeded (did the retry work?)'. It leaves failure_class, family, and confidence unexplained, and notably omits the recommended fix_id, whose per-fix crowd-evidence role is documented only in the schema.

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

Purpose5/5

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

States a specific verb+resource ('Report the outcome of an auto-applied fix') and identifies the caller (the self-heal interceptor) and the trigger (after it gate-applied a fix and retried). It also implicitly distinguishes itself from the sibling what_others_did, which it describes as the consumer of this data.

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

Usage Guidelines4/5

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

Explains the context in which reporting happens (post gate-applied fix + retry) and the two downstream uses of the data, which tells the agent why to submit. It does not state explicitly when an agent should call it manually vs. leaving it to the interceptor, so it falls short of full when/when-not guidance.

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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