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record_interaction

Log tool-call results to measure AI agent reliability. Track success, latency, and errors after every important call.

Instructions

Log one tool-call result. Call this after every important tool use so reliability can be measured.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
successYes
agent_idYes
metadataNo
tool_nameYes
latency_msNo
session_idNo
error_messageNo
Behavior2/5

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

No annotations are provided, so the description must fully disclose behavior. It only says 'log', implying a write, but does not specify persistence, failure modes, error handling, or required permissions. The behavior is under-disclosed for a mutation-like tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short sentences, front-loaded with the primary action and usage instruction. No wasted words or unnecessary detail.

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

Completeness2/5

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

With 7 parameters, no annotations, and no output schema, the description is incomplete. It covers the purpose and timing but omits parameter meanings, return values, and behavioral consequences, leaving significant gaps for an agent to invoke it correctly.

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

Parameters1/5

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

The schema has 0% description coverage, and the description provides no parameter guidance. For 7 parameters, the agent must rely solely on parameter names, which is insufficient for fields like metadata, latency_ms, and error_message.

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?

The description clearly states the action (log), the object (one tool-call result), and distinguishes this tool from sibling reliability/audit tools. It is specific and unambiguous.

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?

The description explicitly instructs to call this tool after every important tool use, providing clear context. It does not mention alternatives or exclusions, but the instruction is direct and sufficient for typical usage.

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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