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Snapback

by io.github.ra1labsworkx-wq
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convert_trace

Read-onlyIdempotent

Turn your raw logs into a Snapback trace so you don't hand-craft JSON. Pass 'source' = a list of log/step entries, or an object with a spans/steps/messages/events/logs array (OTel spans, OpenAI/LangChain message lists, or generic {tool,input,output} arrays all work). Returns {trace} ready to pass straight to diagnose_trace. Free, no token.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hintNooptional: the framework/format, e.g. 'otel', 'openai'
sourceYesyour logs: a list, or an object wrapping a spans/steps/messages array

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • 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"
+}
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive, so the safety profile is covered. The description adds value beyond them: it discloses cost ('Free, no token') and the accepted input shapes, which the agent needs to know to trust and shape the call.

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 purpose, then the input contract, then the return value, all in two dense sentences with no filler. The long parenthetical of accepted formats is information-rich but slightly heavy on a single read.

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

Completeness5/5

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

An output schema exists so return values needn't be detailed, yet the description still flags the return shape and its downstream use. Combined with the cost note and accepted-format list, an agent has everything needed to invoke this correctly.

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 description coverage is 100%, so the baseline is 3, but the description goes further by detailing what 'source' accepts — a bare list, or an object wrapping spans/steps/messages/events/logs, including OTel spans, OpenAI/LangChain messages, and generic {tool,input,output} arrays. The optional 'hint' parameter is only covered by the schema, but the core parameter is well enriched.

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 (convert) and resource (raw logs → Snapback trace) and clarifies the outcome: a JSON trace the user doesn't have to hand-craft. It also routes the agent forward by naming diagnose_trace as the consumer, which cleanly separates it from that sibling.

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 clear context for use — 'so you don't hand-craft JSON' and 'ready to pass straight to diagnose_trace' — and enumerates which input formats qualify. It stops short of stating when NOT to use it (e.g., when logs are already a trace), so it is strong but not fully explicit.

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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