serp-metrics
Server Details
Impression-weighted SERP position, click projection on your own CTR curve, share of voice
- Status
- Healthy
- Uptime
- 100.0% over the last 24 hours
- Last Tested
- Transport
- Streamable HTTP · MCP 2025-06-18
- URL
- ChatGPT
- –
- Claude
- –
TDQS
Scored across 3 tools
Each tool has a clearly distinct purpose: one computes weighted average position, one projects clicks from a user-supplied CTR curve, and one computes share of voice. There is no overlap that would confuse an agent.
All names use snake_case and are descriptive, but they mix noun phrases (impression_weighted_position, share_of_voice) with a verb phrase (project_clicks_from_ctr_curve). No consistent verb_noun pattern, yet names remain readable and predictable enough.
3 tools is well within the ideal 3-15 range. Each tool addresses a distinct analytics need and earns its place without bloat or gaps.
The set covers core SERP analytics: position aggregation, click projection, and share-of-voice. A minor gap is the lack of a direct CTR-computation utility, but the tools are complementary and cover the main workflows without dead ends.
Available Tools
3 toolsimpression_weighted_positionImpression-weighted average positionAInspect
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.
Inputs: rows (required).
project_clicks_from_ctr_curveProject clicks against a supplied CTR curveAInspect
Project how many clicks a set of query rows would earn at a target position instead of their current position, using a CTR curve THAT YOU SUPPLY. This server deliberately ships no built-in CTR table: every published average-CTR-by-position table is a third-party estimate over someone else's traffic, so applying one silently would disguise a guess as a measurement. Pass your own measured curve (Search Console clicks/impressions by position is the honest source). Positions between curve points are linearly interpolated; positions beyond the curve clamp to the last point rather than extrapolate, and are counted in clamped_row_count. If any row carries observed clicks, the output also reports how far the supplied curve is from your reality.
Inputs: rows (required), ctr_curve (required), target_position.
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