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Read-onlyIdempotent

BEFORE running: get known failure patterns for a given agent setup so you can avoid them. Returns a ranked list of {failure_class, root_cause, fix_suggestion} from Snapback's library. Call this before executing a plan and self-correct.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNotask facets, e.g. [tool_calling, retrieval] (optional)
limitNomax cards (default 10)
agent_stackNoe.g. openclaw, langchain (optional)

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

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true and destructiveHint=false, so the safety profile is covered. The description adds meaningful non-structured context: results come from Snapback's library, are ranked, and carry failure_class/root_cause/fix_suggestion, and the tool is meant as a self-correction step before execution.

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?

Three short sentences, front-loaded with the 'BEFORE running' constraint in caps, which is exactly the operative signal. Minor redundancy between 'BEFORE running' and 'Call this before executing a plan', which keeps it from a 5.

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 return values need not be re-explained; with all parameters optional and fully documented, the agent has enough to invoke it. The only gap is the absence of any explicit sibling routing for post-hoc failure diagnosis.

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 100% — tags, limit, and agent_stack are all documented in the schema with examples and defaults. The description adds nothing about how a 'given agent setup' maps to these parameters, so the baseline 3 applies.

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?

States a concrete verb and resource ('get known failure patterns for a given agent setup') plus the returned shape, so the agent knows exactly what it gets. It implies differentiation from the diagnose_* siblings via 'BEFORE running' (preventive vs. post-hoc), but never names a sibling or draws the contrast explicitly, so it falls short of a 5.

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 timing guidance: 'Call this before executing a plan and self-correct.' This tells the agent when the tool belongs in a workflow. There is no statement of when NOT to use it or which sibling to prefer for post-hoc diagnosis, so it is context-rich but not exclusionary.

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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