serp-metrics

Impression-weighted average position

impression_weighted_position

Compute the impression-weighted average SERP position for a set of query rows, alongside the plain arithmetic mean and the gap between them. The arithmetic mean lets a 3-impression query count as much as a 30,000-impression one; the weighted figure describes where a typical impression actually happened. A positive weighting_shift means your high-volume queries rank worse than the unweighted average suggests.

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": {
      "rows": "<rows>"
    },
    "name": "impression_weighted_position"
  }
}

Input Schema

Name	Required	Description	Default
`rows`	Yes	Query rows.

Schema Changelog

Changes observed during successful MCP inspections.

First observedAug 9, 2026

TDQS

A3.8/5.0

Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the mathematical behavior (weighted vs unweighted mean), defines weighting_shift, and interprets its meaning, which goes beyond a simple restatement. However, it does not mention any side effects, edge cases, or output structure.

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 three concise sentences, each providing meaningful information: the computation, the rationale, and the interpretation of a key output. No word is wasted, and the structure flows logically.

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?

The tool has a single parameter with a rich schema and no output schema. The description explains the core outputs conceptually and their interpretation, but it does not specify the exact return structure or handle potential edge cases. This is largely complete for a straightforward computation tool.

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?

The schema already documents all fields within the rows parameter with 100% coverage. The description does not add per-parameter details beyond what the schema provides, but it does provide context on how the data is used (e.g., positions and impressions). This matches the baseline for high schema coverage.

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 tool computes impression-weighted average SERP position, arithmetic mean, and the gap between them. It is specific and unambiguous, but it does not explicitly mention how it differs from sibling tools like share_of_voice or project_clicks_from_ctr_curve, so it lacks explicit sibling differentiation.

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 description implies the tool is used when analyzing query rows with position and impression data, but it does not provide explicit when-to-use guidance or mention any alternatives. The context is understandable but no exclusions or clear alternatives 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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