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by TeamUnilytics
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JavaScript
Remote

aeo_audit_prompts

Read-only

Audit tracked-prompt sets to classify intent, assess realistic metrics, and get keep, cut, or rewrite recommendations with suggested plays.

Instructions

Audit a tracked-prompt set — per-prompt intent, realistic metric, play, and a keep/cut/ rewrite recommendation. prompts = [{id, text, intent?, mention_rate?, citation_rate?}], at most 80 per call, each text up to 300 characters. With use_llm (the default) the prompts are sent to OpenAI for classification.

[Provider: OpenAI]

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "prompts": "<prompts>"
    },
    "name": "aeo_audit_prompts"
  }
}

Input Schema

NameRequiredDescriptionDefault
promptsYes
use_llmNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.1.0
    • removedInput schema / properties / config
      Removed value: -{
-  "default": null,
-  "title": "config",
-  "type": "string"
-}
  2. First observedv1.0.0

TDQS

B3/5.0
Behavior1/5

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

The description says prompts are sent to OpenAI for classification when use_llm is enabled, which is an external service interaction, while annotations declare openWorldHint=false. This is a direct contradiction between the disclosed behavior and the structured annotation. The useful limits on prompt count and text length do not offset that annotation conflict.

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 front-loaded and compact. Every sentence adds either purpose, parameter constraints, or behavioral context, with no filler.

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?

There is no output schema, and the description only names the return categories rather than explaining output structure or field meanings. It gives enough to invoke the tool, but not enough to fully predict the response shape or detailed return semantics.

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 coverage is 0%, so the description carries the full burden and does a good job: it defines the prompts object shape, optional field markers, the 80-item cap, the 300-character text limit, and that use_llm defaults to true. It could still say more about allowed values for intent and rate fields and about behavior when use_llm is false.

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 verb and resource: audit a tracked-prompt set and produce per-prompt intent, metrics, plays, and a keep/cut/rewrite recommendation. It is specific enough to distinguish the tool's function, but it does not explicitly differentiate it from siblings such as aeo_filter_prompts or aeo_cluster_prompts.

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?

It implies the tool is for auditing prompts, but it gives no explicit when-to-use versus alternatives guidance. Sibling tools like aeo_generate_prompts, aeo_filter_prompts, and aeo_cluster_prompts are not mentioned as alternatives or exclusions.

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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