Quattr

striking_distance

Read-only

Find your "striking distance" keywords — the ones ranking just off page 1 (positions 8–20 by default) where a small rank nudge could win real clicks — and size the upside. Uses THIS site's OWN fitted CTR curve (modeled by ctr_curve_model, not a generic published curve): upside = impressions × (expected CTR at the target position − expected CTR at the current position), then an AI-Overview haircut is applied and AIO-dominated SERPs are down-weighted. Ranked by haircut-adjusted upside. Use for "what's our quickest-win SEO opportunity?" / "which keywords are closest to page 1?". The estimate is modeled, not a promise — AIO can erase the gain.

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": {
      "date_range": "<date_range>"
    },
    "name": "striking_distance"
  }
}

Input Schema

Name	Required	Description	Default
`brand`	No	Restrict candidates to branded ("Yes") or non-branded ("No") demand. Non-branded is usually the actionable opportunity.
`top_n`	No	How many ranked candidate keywords to return.
`device`	No	Restrict candidates to one device.
`intent`	No	Restrict candidates to one intent (a customer keyword group). Resolve the id with resolve_entity first.
`source`	No	Where Google search performance data is read from. Use "bigquery" ONLY when the user explicitly asks for BigQuery as the source AND describe_data_sources reports it available from live customer configuration — it returns far more granular per-query data (privacy-suppressed impressions appear under "[anonymized by google]").	default
`band_max`	No	Upper bound of the candidate position band (inclusive). Default 20.
`band_min`	No	Lower bound of the candidate position band (inclusive). Default 8 — just off page 1.
`date_range`	Yes	Explicit date range, e.g. "2026-05-01 to 2026-05-31". Both endpoints are INCLUSIVE — the END day is covered (a 7-day range = 7 days). Quattr weeks run Sunday–Saturday; for "last week" use the most recent complete Sun–Sat span.
`aio_haircut`	No	Approximated AI-Overview haircut (0–1) applied to the modeled CTR gain to reflect AIO compressing organic clicks (default 0.3 = a 30% discount). Flagged as an approximation, not a measured value.
`target_position`	No	The position to model nudging each candidate UP to (default 5 — bottom of the page-1 cluster). Upside is the CTR gain from current → target.
`min_bucket_instances`	No	Per-bucket reliability floor for the underlying curve fit (default 5).

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 19, 2026

TDQS

A4.3/5.0

Behavior5/5

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

Annotations already declare a safe read-only profile, and the description adds substantial behavioral context beyond them: the AIO haircut is flagged as an approximation (not measured), the estimate is explicitly 'modeled, not a promise,' AIO-dominated SERPs are down-weighted, and the brand/source caveats are surfaced. This goes well past what the structured fields convey.

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?

Front-loads the definition of 'striking distance' before methodology and use cases, which is the right ordering. It is dense with em-dashes and parentheticals, and slightly long, but each sentence (formula, haircut, ranking, use cases, caveat) contributes materially.

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 an 11-parameter, single-required-arg analytical tool with no output schema, the description covers purpose, methodology, defaults, and limitations adequately, and notes the result is ranked by haircut-adjusted upside. It does not detail the returned record fields, but with no output schema that gap is minor and largely implied.

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 coverage is 100% and the schema descriptions are already detailed (defaults, inclusive bands, BigQuery caveat). The description mostly restates the 8–20 default band rather than adding new parameter-level meaning, so the baseline 3 is appropriate.

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 and resource (find striking-distance keywords ranking 8–20, size the click upside) and operationalizes it with the exact formula, so an agent knows precisely what it computes. It distinguishes itself from neighbors like ctr_curve_model (which it names as the dependency producing the curve) and from ranking-report tools.

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

Usage Guidelines4/5

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

Gives concrete natural-language triggers ('what's our quickest-win SEO opportunity?', 'which keywords are closest to page 1?') that map directly to the tool. However, it never states when NOT to use it or names a specific sibling alternative (e.g., seo_keyword_gap_analysis or seo_ranking_movement_report), so routing is context-rich but not exclusionary.

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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