Local Grid Rank Tracker

Local Grid Rank Tracker

AGENT ROLE: Autonomous local-rank agent. Sample rankings across a coordinate grid and emit the JSON in references/output.schema.json, including a per-point rank matrix and aggregate metrics. Local rank is proximity-dependent — a single point is never the whole picture.

OBJECTIVE

For a business and a keyword, measure where in its service area it appears in the local pack / map results, quantify how visibility decays with distance, and summarize with average grid rank and share-of-top-3.

INPUTS

• business_name (REQUIRED): the exact GBP name to match in results.
• keyword (REQUIRED): the search term (e.g., "emergency plumber").
• center (REQUIRED): { lat, lng } of the business (or service-area center).
• grid_size (OPTIONAL, default 7): odd N for an N×N grid (49 points at 7).
• spacing_km (OPTIONAL, default 1.5): distance between adjacent grid points.
• zoom (OPTIONAL, default 14): map zoom passed to the SERP.

AUTHENTICATION (SERP API)

1. REQUIRE env SERP_API_KEY. IF unset THEN STOP error.code="AUTH_MISSING_API_KEY".
2. Endpoint (SerpApi shape): GET https://serpapi.com/search.json?engine=google_maps&q={keyword}&ll=@{lat},{lng},{zoom}z&type=search&api_key={key} → local_results.
3. Grid points are computed locally from center + spacing_km (no geocoding key needed since center is provided; if only an address is given, geocode via a keyless service first and note geocoded=true).

EXPECTED TOOL CALLS

• Run scripts/grid_rank.py --name "..." --keyword "..." --lat .. --lng .. --grid 7 --spacing 1.5.
• One SERP fetch per grid point (N×N total); parse local_results order to find the business's rank.

PROCEDURE (deterministic)

STEP 1 — BUILD GRID: N×N points centered on center, offset by spacing_km per step (convert km→degrees: lat /111, lng /(111*cos(lat))). STEP 2 — For each point: fetch google_maps local results at that ll; find the business by fuzzy-normalized name match; record its 1-based rank (or >20/not_found). STEP 3 — MATRIX: assemble the N×N rank matrix (rows N→S, cols W→E) for the heatmap. STEP 4 — AGGREGATE: avg_rank over found points; share_of_top3 = points_in_top3 / total_points; visibility_falloff = correlation of rank vs distance from center. STEP 5 — EMIT the matrix + aggregates + the center point's rank.

RATE LIMITS & ERROR HANDLING

• N×N can be many billed searches (49 at grid 7). On 429/quota THEN backoff 2^attempt (max 5) then STOP error.code="RATE_LIMITED" and return the partial matrix filled so far (partial_points).
• Cap concurrency at 2; a 7×7 grid is 49 searches — WARN in output if grid_size >= 9 (81+ searches) via cost_note.
• Per-point 5xx/timeout retry ≤2 then mark that cell null (unqueried) — never abort the whole grid.

MISSING / INSUFFICIENT DATA

• IF the business is absent from a point's local results THEN that cell = not_found (a real, meaningful result — it means no visibility there), distinct from null (query failed).
• Name matching uses normalized fuzzy compare; record match_confidence per found cell; below threshold → treat as not_found.
• Never fabricate a rank; absence is not_found.

OUTPUT

One JSON object per references/output.schema.json. No prose.

FILES

• scripts/grid_rank.py — grid generation, per-point SERP fetch, name matching, aggregation.
• references/output.schema.json — output contract.