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Statistically rigorous weather and climate context: forecast plus 35-year historical ranking.
About
Statistically rigorous weather and climate context: forecast plus 35-year historical ranking.
Security Report
Valid MCP server (1 strong, 4 medium validity signals). No known CVEs in dependencies. Imported from the Official MCP Registry. 1 finding(s) downgraded by scanner intelligence.
4 files analyzed · 1 issue found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
Permissions Required
This plugin requests these system permissions. Most are normal for its category.
What You'll Need
Set these up before or after installing:
Environment variable: BASELINE_API_URL
Environment variable: BASELINE_API_KEY
How to Install
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-elninyo-ops-baseline-mcp": {
"env": {
"BASELINE_API_KEY": "your-baseline-api-key-here",
"BASELINE_API_URL": "your-baseline-api-url-here"
},
"args": [
"baseline-mcp"
],
"command": "uvx"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
baseline-mcp
MCP server exposing Baseline as agent tools — statistically rigorous weather and climate context, not just current conditions. Thin translation layer only: no climate logic lives here, every tool call is an HTTP request to the Baseline API. See baseline_mcp_server_plan.md in the Baseline project for the full design, and METHODOLOGY.md for how the underlying data and rankings are computed.
Tools
get_climate_context— natural-language weather and climate questions, full context back (forecast + 35-year historical percentile ranking).get_context_for_coordinates— same, for an exact lat/lon rather than a place name.get_water_year_status— precipitation/temperature status since the start of the water year (Oct 1 US / Jan 1 elsewhere), ranked against 35 years.compare_to_normal— how unusual current or forecast conditions are at one location.compare_locations— rank precipitation, temperature, or snowfall across 2-10 locations (or a curated category likecolorado_ski_resorts) in a single call, over a water year, season, month, or ski season.
Installation
Requires a Baseline API key. Self-serve signup isn't available yet — during this early period, contact Chad McNutt (chadmcnutt@gmail.com) for a key.
pip install baseline-mcp
# or: uvx baseline-mcp
Then add it to your MCP client's config, with your API key:
Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json):
{
"mcpServers": {
"baseline": {
"command": "uvx",
"args": ["baseline-mcp"],
"env": {
"BASELINE_API_URL": "https://api.baselinecontext.com",
"BASELINE_API_KEY": "your-key-here"
}
}
}
}
Claude Code: claude mcp add baseline --env BASELINE_API_URL=https://api.baselinecontext.com --env BASELINE_API_KEY=your-key-here -- uvx baseline-mcp
Cursor (.cursor/mcp.json or global MCP settings): same shape as the Claude Desktop config above, under whatever key Cursor's MCP settings use for server name.
Local development
The venv lives outside this directory (~/.venvs/baseline-mcp) rather than in .venv/ here, because this project sits under iCloud-synced ~/Documents — iCloud evicts/re-materializes files inside large venvs unpredictably, which causes intermittent ModuleNotFoundErrors. Keep it that way.
python3 -m venv ~/.venvs/baseline-mcp
~/.venvs/baseline-mcp/bin/pip install -e .
cp .env.example .env # fill in BASELINE_API_URL and a free_api-tier BASELINE_API_KEY
Run against a local Baseline instance (python3 app.py in ../baseline), then:
~/.venvs/baseline-mcp/bin/mcp dev src/baseline_mcp/server.py
Status
All 5 tools built and tested against a live local Baseline instance, including tool-selection validation in Claude Desktop. Verified end-to-end against the production Baseline API as of the 2026-08-01 soft-launch prep (data freshness, temporal query handling, and climatology all fixed and confirmed live).
The Baseline API each tool wraps was additionally exercised against production the week of 2026-08-05 through a ~150-question adversarial stress-test corpus (spatial, temporal, and phrasing edge cases) — real bugs were found and fixed at every layer this server depends on: location extraction, temporal-window parsing, and geocoding, all deployed and reverified live, zero regressions.
METHODOLOGY.md (trust collateral) complete. Production now served over TLS at api.baselinecontext.com. See baseline_mcp_server_plan.md in the Baseline project for full task history. Published to PyPI as of 0.1.2.
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