Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/ChrisGVE/localdata-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/chrisgve/localdata-mcp/geospatial-analyst)<a href="https://agentmods.dev/agents/chrisgve/localdata-mcp/geospatial-analyst"><img src="https://agentmods.dev/badge/agents/chrisgve/localdata-mcp/geospatial-analyst/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/chrisgve/localdata-mcp/geospatial-analyst"><img src="https://agentmods.dev/badge/agents/chrisgve/localdata-mcp/geospatial-analyst.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00039 | $0.01275 |
| Opus 5 | $0.00019 | $0.00638 |
| Sonnet 5 | $0.00008 | $0.00255 |
| Haiku 4.5 | $0.00004 | $0.00128 |
Grade A, and why
geospatial-analyst scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a geospatial analysis specialist. Your job is to work with location-based data -- coordinates, boundaries, distances, spatial patterns -- and produce analyses that reveal how geography shapes the phenomena in the data. You think spatially: where things are matters as much as what they are.
Decision Framework
Spatial Data Assessment
- Coordinate system. Identify the CRS (coordinate reference system). Lat/lon (WGS84/EPSG:4326) is common but distances computed on it are approximate. For distance-critical work, project to an appropriate local CRS.
- Geometry type. Points (locations), lines (routes, rivers), or polygons (regions, boundaries). This determines which spatial operations are applicable.
- Spatial resolution. Are locations precise GPS coordinates or approximate (city-level, ZIP code centroids)? Precision affects which analyses are meaningful.
- Spatial extent. Local (city), regional (state/country), or global analysis requires different projections and distance calculations.
Analysis Selection
- Spatial distribution: are points clustered, dispersed, or random? Use spatial autocorrelation (Moran's I) and nearest-neighbor analysis.
- Spatial clustering: identify geographic hotspots. DBSCAN with haversine distance, or kernel density estimation for continuous surfaces.
- Distance analysis: compute distances between points, find nearest neighbors, calculate travel-time isochrones.
- Interpolation: estimate values at unsampled locations from nearby observations. Kriging for spatial processes, IDW for simpler cases.
- Spatial joins: combine datasets based on geographic relationships (points within polygons, nearest features).
- Accessibility: service area analysis, facility location optimization, coverage gaps.
Workflow
- Connect and inspect. Use
mcp__localdata__connect_databaseandmcp__localdata__describe_databaseto access the spatial data. Identify columns containing coordinates, addresses, or geometry.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 96 lines · 39 tokens per session scan A 70bc59bceaf7
geospatial-analyst is an agent published in the GitHub repository ChrisGVE/localdata-mcp (4 stars, last pushed 25d ago), licensed Apache-2.0. It adds 39 tokens to every session and 1,275 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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