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Benchmark AI sentiment against rivals

get_ai_sentiment_benchmark
Read-only

Compare how favorably AI answers describe the brand and its rivals.

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": {
      "projectId": "<projectId>",
      "siteId": "<siteId>"
    },
    "name": "get_ai_sentiment_benchmark"
  }
}

Input Schema

NameRequiredDescriptionDefault
toNo
fromNo
rivalNo
engineNo
siteIdYes
enginesNo
projectIdYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.7/5.0
Behavior2/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds nothing beyond the title: no word on default time window, how sentiment is scored/aggregated, or whether rivals must be pre-configured. For a read-only analytics tool with annotation coverage this is a modest but real gap.

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?

One front-loaded sentence with no filler or redundancy. It is efficient but arguably over-compressed given the tool's 7 parameters, leaving no room for the operational context an agent needs.

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

Completeness2/5

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

With 7 parameters, no output schema, and zero schema descriptions, the description should carry the burden of explaining the comparison dimensions and what a benchmark result contains. It doesn't, so an agent can't predict the shape or scope of the return value.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% across 7 parameters, and the description only loosely gestures at the `rival` parameter through the word "rivals." It says nothing about from/to date range behavior, the engine/engines distinction, or the mandatory projectId/siteId scoping, so it fails to 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?

A specific verb ("Compare") applied to a specific resource (how favorably AI answers describe the brand vs. rivals), so the agent knows this is a comparative sentiment analysis rather than a raw listing. It doesn't explicitly distinguish itself from close siblings like get_ai_sentiment_overview or get_ai_sentiment_driver, leaving overlap for the agent to resolve.

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?

No indication of when to reach for this tool versus get_ai_sentiment_overview, get_ai_sentiment_driver, or get_ai_visibility_competitor. No prerequisites (e.g., needing a siteId/projectId scope) or exclusions are stated.

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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