Rankwise

Add a question

add_question

Track a new question for a domain, e.g. "best invoicing software for freelancers". Scheduled checks then ask the plan's AI engines whether they cite the brand. Fails at the plan's question limit. Free: it uses no credits.

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": {
      "domain_id": "<domain_id>",
      "question": "<question>"
    },
    "name": "add_question"
  }
}

Input Schema

Name	Required	Description
`locale`	No	Market locale such as en-US or de-DE. Omit for the default market.
`question`	Yes	The question, as a user would ask an AI engine.
`domain_id`	Yes	The tracked domain's id, from list_domains.

Output Schema

Name	Required	Description	Default
`id`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 20, 2026

TDQS

A4/5.0

Behavior4/5

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

Goes beyond the annotations by disclosing the downstream effect (scheduled checks ask AI engines whether they cite the brand), a failure mode ("Fails at the plan's question limit"), and cost transparency ("Free: it uses no credits"). Annotations already cover the read/write and idempotency profile, so this added operational context is the value layer.

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?

Three tight sentences, front-loaded with the core action and example, followed by behavior and cost. Every sentence carries distinct information with no redundancy.

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?

With an output schema present (so return values need not be described) and annotations covering safety, the description supplies purpose, behavior, limit, and cost. Only the relationship to alternatives like list_questions/remove_question is missing, a minor gap.

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%, so the schema already documents domain_id, question, and locale fully. The description adds only an example question string, which marginally clarifies format but does not go beyond the schema baseline.

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?

States a specific verb+resource ("Track a new question for a domain") and reinforces it with a concrete example of the question format. This is clearly distinguishable from siblings like add_domain, add_keywords, remove_question, and list_questions without opening any schema.

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?

The purpose implies when to use it (adding a new tracked question versus removing/listing existing ones), but there is no explicit when-to-use statement and no named alternatives. Guidance is left to inference rather than spelled out.

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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