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cloudflare-mcp

by pocc

get_ai_gateway_evaluation

Retrieve detailed evaluation results for an AI Gateway, including performance metrics and outcomes, to analyze and troubleshoot AI model responses.

Instructions

Get details of an AI Gateway evaluation

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
account_idYesThe account ID
gateway_idYesThe AI Gateway ID
evaluation_idYesThe evaluation ID

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.0.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral disclosure burden. 'Get details' implies a read-only operation but adds no context about the return payload, not-found/error behavior, or permission requirements. The description is nearly a paraphrase of the tool name and discloses nothing beyond it.

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?

The description is a single sentence with zero wasted words and the core operation is front-loaded. It is appropriately terse, though the same space could have carried routing guidance (e.g., naming list_ai_gateway_evaluations) without hurting conciseness.

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

Completeness3/5

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

For a simple 3-parameter getter, the definition is minimally adequate: the operation and all parameters are clear. However, with no output schema and no annotations, the description does not explain what 'details' are returned or where evaluation_id comes from, leaving an agent to rely on conventions.

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%, with account_id, gateway_id, and evaluation_id each already documented. The description adds no parameter-level meaning beyond what the schema provides, so the high-coverage baseline of 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?

The description uses a specific verb ('Get') and a specific resource ('details of an AI Gateway evaluation'), making the core operation unambiguous. It correctly implies a fetch-by-ID operation as opposed to a list operation, but it does not explicitly name sibling alternatives like list_ai_gateway_evaluations or get_ai_gateway_logs, so differentiation is left to inference.

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 no guidance on when to use this tool versus its siblings. It does not state that it fetches a single evaluation by ID, nor that evaluation_id would typically come from a prior list_ai_gateway_evaluations call. An agent must infer usage entirely from the parameter names and naming conventions.

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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