Snapback

what_others_did

Read-onlyIdempotent

THE CROWD: for a failure_class (or pass the family/error and we'll map it), see what OTHER agents did about the same failure and whether it worked — anonymized, aggregated across everyone. Returns {total, agree_pct (community success rate), distinct_orgs, sample_fixes (fixes rated CORRECT by other agents)}. Use it when you hit a failure and want the crowd's verdict on what actually fixes it, not just the single library answer. Free, no token. Privacy-safe: only aggregate counts + a community success rate + the working fixes — never any org, agent, or trace identity. Hidden below a small min-sample so a single report can't be reverse-engineered. This is the network effect: the more agents use Snapback, the sharper this answer gets.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`error`	No	optional: an error string — we'll diagnose it to find the failure_class, then return the crowd outcomes for it
`failure_class`	No	the failure_class to look up (e.g. 'unhandled_tool_error', 'loop_repeated_tool_call'); or pass 'error' and we map it

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

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

A4.5/5.0

Behavior5/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, openWorld), and the description adds substantial context beyond them: it's free/no-token, anonymized and aggregated, privacy-safe (never exposes org/agent/trace identity), and suppressed below a min-sample threshold. That is exactly the kind of behavioral disclosure annotations cannot provide.

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-loads the purpose and the return shape, and every sentence is mostly functional. The closing 'This is the network effect…' line is promotional filler rather than operational guidance, costing a point.

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?

An output schema exists so the return fields needn't be re-explained, and the description still lists them (mildly redundant). Combined with full param coverage and rich behavioral notes, the definition is complete enough for an agent to call it correctly; the only gap is the absence of an explicit alternative-tool routing statement.

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

Parameters4/5

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

Schema coverage is 100% and both params are documented, so baseline is 3. The description adds real value by explaining that failure_class and error are alternative inputs and that passing error triggers a diagnostic mapping step, plus naming example failure_class values. That goes beyond what the schema states, though it doesn't fully enumerate the mapping behavior.

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 and resource: look up what OTHER agents did about a given failure_class and whether it worked. This is clearly distinguishable from siblings like get_verdict, search_docs, and diagnose_trace because it emphasizes anonymized cross-agent aggregation.

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?

Gives a clear triggering condition — use it when you hit a failure and want the crowd's verdict rather than 'the single library answer' — which implicitly contrasts with the single-source sibling tools. It stops short of naming a specific alternative tool or stating when not to use it, so it falls short of 5.

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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