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Metis · Data Guardian — Probe Tool Result

probe_tool_result

Examine external content for injection patterns before adding to agent context. Detects and reports suspicious patterns to prevent malicious input.

Instructions

M5.7.1 — Probe external tool result for injection patterns.

Call this before inserting any externally-sourced content into agent context:
web scrapes, RSS items, PDF extracts, YouTube transcripts, GitHub readmes.

Args:
    content: The external content to probe.
    source_label: Human-readable label for logging (e.g., "PubMed abstract", "RSS item").

Returns:
    JSON with {probed_content, flagged, patterns_found}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYes
source_labelNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations provided, so description is the sole source. It describes a non-destructive probe (returns flagged/patterns_found) but does not explicitly state it is read-only or safe. Lacks detail on logging side effects or performance implications.

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?

Well-structured with version header, purpose, usage, args, and returns. Concise with no extra fluff. Could be slightly more streamlined but overall efficient.

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?

Provides enough context given the simple nature of the tool: clear what it does, when to use, and what to expect in returns (probed_content, flagged, patterns_found). No missing critical information for an AI agent to use it 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 0%, but the description explains both parameters: 'content' as the external content to probe and 'source_label' for logging. This adds significant meaning beyond the bare schema.

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 clearly states the tool probes external content for injection patterns and gives specific use cases (web scrapes, RSS, etc.), making the purpose distinct from many sibling tools. However, it doesn't explicitly differentiate from close siblings like 'check_data_safety'.

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?

Explicitly instructs to call this before inserting externally-sourced content into agent context, with concrete examples. Does not mention when not to use or alternatives, but the usage guidance is clear and actionable.

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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