Puley

Does AI recommend you?

ai_recommends

Whether AI assistants recommend a business: asks OpenAI's and Google's models, with web search on as ChatGPT and Gemini answer people, 3 questions a buyer asks about what the business sells, and shows who each answer recommends first, whether the business is named, and the sites the answers quoted. what: what is sold (CRM for small businesses, dentist in Austin TX). business: its name or website. questions: the person's own questions instead (up to 3). Saved in the user's Puley history.

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": {
      "what": "<what>"
    },
    "name": "ai_recommends"
  }
}

Input Schema

Name	Required	Default
`what`	Yes
`country`	No	US
`business`	No
`questions`	No

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 9, 2026

TDQS

A3.7/5.0

Behavior4/5

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

Annotations already flag a non-read-only, open-world, non-destructive call, and the description usefully explains why: results are 'Saved in the user's Puley history', disclosing the persistence side effect. It also describes the return content (top recommendation, whether the business is named, quoted sites), valuable since there is no output schema. It omits latency/cost implications of querying two external models.

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

Conciseness3/5

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

The purpose is front-loaded, but the body is a single sprawling sentence with awkward phrasing ('with web search on as ChatGPT and Gemini answer people') and parameter notes tacked on inline. Every element is relevant, yet the packaging makes it harder to scan than it needs to be.

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?

For a four-parameter tool with no output schema, the description covers what the tool does, what it returns, and the persistence side effect, which is close to complete. The gaps are the undocumented 'country' parameter and the absent comparison with 'get_recommended_by_ai'.

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?

With 0% schema description coverage the description must carry all parameter meaning, and it largely does: 'what' gets concrete examples (CRM for small businesses, dentist in Austin TX), 'business' is name or website, and 'questions' substitutes the user's own questions capped at three. Only 'country' (default US) is left undocumented.

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 gives a concrete verb chain and resource: it asks OpenAI and Google models (with web search) three buyer-style questions and reports who is recommended first, whether the business is named, and which sites were quoted. That is far more specific than the name alone. It does not, however, distinguish itself from the very similar sibling 'get_recommended_by_ai', leaving an agent unsure which of the two to call.

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

Usage Guidelines3/5

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

Usage is implied by the scenario (a business owner checking AI visibility), and 'questions: the person's own questions instead' hints at a variant mode. But there is no explicit when-to-use, when-not-to-use, or comparison against the nearly identical 'get_recommended_by_ai' sibling, forcing the agent to infer.

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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