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SARAMALI15792

UAAR University MCP Server

log_interaction

Record AI agent interactions for audit trails by capturing agent ID, tool usage, and operational details to ensure accountability.

Instructions

Log an AI agent interaction for auditing purposes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_idYes
toolYes
detailsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

Annotations indicate this is a non-readOnly, non-destructive, non-idempotent, open-world operation. The description adds value by clarifying it's for 'auditing purposes,' which implies persistence and record-keeping, but doesn't elaborate on side effects, rate limits, or authentication needs beyond what annotations cover.

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?

The description is a single, clear sentence that efficiently conveys the tool's purpose without unnecessary words. It's front-loaded and wastes no space, making it easy to parse quickly.

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?

Given the tool's moderate complexity (3 parameters, annotations provided, output schema exists), the description is somewhat complete but lacks details on parameter usage and behavioral context. The presence of an output schema means return values don't need explanation, but more guidance on when and how to use the tool would improve completeness.

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 0%, but the description doesn't explain parameters like 'agent_id', 'tool', or 'details'. It relies on the schema's titles and types, providing no additional semantic context. With three required parameters and no schema descriptions, the baseline is 3 as the description doesn't compensate for the coverage gap.

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 action ('Log') and purpose ('for auditing purposes'), specifying it's for AI agent interactions. It distinguishes from siblings by focusing on logging rather than administrative or query operations, though it doesn't explicitly contrast with similar tools since none exist in the sibling list.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides minimal guidance, stating only the general purpose ('for auditing purposes') without specifying when to use it versus alternatives, prerequisites, or exclusions. No explicit alternatives are mentioned, and the context is implied rather than detailed.

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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