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Outdooriq MCP logo
Health: ActiveRecent health check succeeded.Last checked 9/7/2026, 6:39:33 PM

Outdooriq MCP

User RatingsBe the first to rate and review this MCP server! Enrichment pendingWe haven’t run our AI enrichment pass on this listing yet, so the overview, use cases, and FAQ below may be sparse or missing. We work through the catalog over time β€” check back soon.
View RepositoryVisit Website

Paid MCP for fishing/lake/stocking intelligence β€” 72k+ US lakes, 293k+ events.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

Add to CursorAdd to VS Code
Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag β€” we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON β–Ύ

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "outdooriq-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote"
      ]
    }
  }
}

πŸ’‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Directory Badge Claim listing AlternativesπŸ’» More in Developer Tools

Documentation Overview

OutdoorIQ MCP β€” Outdoor Recreation Intelligence

Powered by 72,000+ US lakes and 293,000+ stocking events across 12 states.

OutdoorIQ MCP is a paid MCP server that exposes lake conditions, fish-stocking records, fishing-favorability scoring, and live weather to AI agents. It is built on the CastIQ dataset (the same data that powers fishing-seo.pages.dev and the CastIQ catalog APIs).

If you're building a trip-planning assistant, a fishing app, a travel concierge, or an outdoor-brand agent that needs to recommend lakes, time visits to recent stocking events, or pull a fishing report on demand, this is the data layer you wire up.

For consumer-facing AI products, OutdoorIQ replaces a ten-source ETL with one authenticated MCP endpoint β€” and bills predictably so you don't get a surprise S3 invoice the first weekend traffic spikes.


Pricing

TierPriceLimits
Free$0outdooriq-dev-key-001, 50 calls/day
Pro$14/moUnlimited
Pay-as-you-go$0.01/callNo monthly minimum

Listing & checkout: https://mcpize.com/outdooriq-mcp


Install in Claude

Live URL: https://mcp.castiq.net/mcp (Railway fallback: https://web-production-9b8950.up.railway.app/mcp)

The fastest path uses mcp-remote as a stdio→HTTP bridge:

Terminal
claude mcp add outdooriq-mcp -- npx -y mcp-remote \
  https://mcp.castiq.net/mcp \
  --header "X-API-Key:outdooriq-dev-key-001"

Or configure manually:

config.json
{
  "mcpServers": {
    "outdooriq-mcp": {
      "url": "https://mcp.castiq.net/mcp",
      "headers": { "X-API-Key": "outdooriq-dev-key-001" }
    }
  }
}

Anthropic MCP Registry entry: io.github.bch1212/outdooriq-mcp β€” listed at https://registry.modelcontextprotocol.io.

Example agent prompt:

"Find top 5 trout lakes near Chicago for this weekend."

The agent calls get_nearby_lakes (lat/lng of Chicago, radius 200mi), filters for species: trout via get_top_lakes, then pulls get_fishing_report_summary for the top match.


Tool Reference

ToolArgsReturns
search_lakesname?, state?, county?, min_acres?, max_acres?, limit?List of matching lakes
get_lake_detailslake_idCoords, acreage, depth, species, facilities
get_stocking_datalake_id?, species?, year?, limit?Recent stocking events
get_fishing_scorelake_id0-100 score with bucket breakdown
get_nearby_lakeslat, lng, radius_miles?, min_score?, limit?Lakes near GPS, sorted by score
get_weather_for_lakelake_idCurrent + 7-day forecast (Open-Meteo)
get_top_lakesstate?, species?, limit?Highest-scoring lakes
get_stocking_schedulestate?, species?, month?, limit?Most-recent matching events as a planning prior
search_speciesstate?, season?Actively-stocked species
get_fishing_report_summarylake_idNatural-language report

Architecture

Code
client (Claude / agent)
        β”‚  HTTP POST /mcp  (JSON-RPC 2.0)
        β–Ό
   FastAPI app (server.py)
        β”‚  X-API-Key auth + per-day rate limiter
        β–Ό
  Tool registry (10 tools)
        β”‚
        β–Ό
  db.connection.py  ──────┐
        β”‚ Postgres mode  β”‚  β†’ CastIQ Postgres (72k lakes, 293k stockings)
        β”‚ SQLite fallbackβ”‚  β†’ bundled seed (100+ lakes, ~150 stockings)
        β–Ό
  tools.* (lakes, stocking, scoring, weather, reports)
        β”‚
        β–Ό
  Open-Meteo (no key)

Postgres vs SQLite mode

The server picks a backend at startup:

  1. If DATABASE_URL is set, it tries asyncpg.create_pool. On success β†’ logs [OutdoorIQ] Running in Postgres mode.
  2. If the pool fails (timeout, bad creds, DB down) or DATABASE_URL is unset, the server seeds an in-memory SQLite DB with 100+ real lakes and logs [OutdoorIQ] Running in SQLite fallback mode.

The server always starts, regardless of Postgres availability.

CapabilityPostgres modeSQLite fallback
Lake catalog72,669 (12 states)~106 (WI, MN, IL, IA, MO)
Stocking events293,821 historical~150 templated, recency-tuned
Species coverageAll states/species in CastIQCurated subset (walleye, bass, musky, trout, crappie, perch, pike, panfish, salmon, lake_trout, sauger, white_bass, sturgeon, catfish, bluegill, smallmouth_bass)
Year-over-year analysisYes β€” multi-year stockingsLimited β€” events are anchored relative to "now"
Stocking-schedule toolReal historical patternsApproximated from seed
Weather, scoring, reportsIdenticalIdentical

Local development

server.ts
git clone <this repo>
cd mcp-outdoors
python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

# Run with SQLite fallback (no DATABASE_URL needed)
python -m run

# OR run against the CastIQ Postgres
export DATABASE_URL=postgresql://vikinetic:vikinetic_dev@localhost:5444/vikinetic
python -m run

Then:

Terminal
curl -s http://localhost:8080/health
curl -s -X POST http://localhost:8080/mcp \
  -H "X-API-Key: outdooriq-dev-key-001" \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/list"}'

Tests

bash
pytest -v

The test suite covers both the SQLite fallback path and the Postgres dispatch path (via a mocked asyncpg pool), plus auth, rate limits, all 10 tools, JSON-RPC initialize / list / call, and the scoring algorithm's bucket math.


Deploy to Railway

A deploy.sh script is included at the repo root. Run it on your Mac (the Cowork sandbox can't reach Railway/Stripe/Cloudflare APIs):

bash
./deploy.sh

It expects RAILWAY_API_TOKEN (or RAILWAY_TOKEN exported as RAILWAY_API_TOKEN), points the project at nixpacks.toml, and sets the DATABASE_URL env var if you've also provisioned the CastIQ Postgres on Railway.

Railway gotcha (already handled): Railway exec's startCommand without a shell, so $PORT doesn't expand. We use python -m run and read PORT from os.environ inside run.py.


MCPize listing copy

OutdoorIQ MCP β€” the data layer for outdoor-rec AI agents. 72,000+ US lakes, 293,000+ fish-stocking events, and live weather behind one authenticated endpoint. Search lakes by name, state, or acreage; pull stocking history filtered by species and month; compute a 0-100 fishing-favorability score that bakes in recency, species diversity, lake size, and current conditions. One JSON-RPC call replaces a multi-source ETL.

Built for fishing apps, trip-planning assistants, travel concierges, and outdoor-brand agents. $14/mo Pro for unlimited use, or pay $0.01 per call. Free dev tier (50 calls/day) lets you ship a prototype before opening your wallet. Install with claude mcp add outdooriq-mcp --url https://mcp-outdoors.up.railway.app/mcp.


License

MIT. See LICENSE.

Read the full README β†’View source on GitHub β†’

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Reviews

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Frequently Asked Questions about Outdooriq MCP

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "outdooriq-mcp": { "command": "npx", "args": ["-y", "outdooriq-mcp"] } }

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Technical Specs & Signals

CategoryπŸ’»Developer Tools
More technical detailsExpand β–Ύ
TransportSTDIO
RuntimeNode.js
Last updatedSep 7, 2026
Views0
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GitHub Star CountTotal stargazers on GitHub representing community popularity (0 stars).
35Quality signal: Fair Β· 35/100How this signal is calculated β–Ύ
Server availabilityNot measured

Not scored for repo-hosted servers β€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools15/30
Adoption & activity1/15
Community engagement0/10

A guidance signal from public completeness & health data β€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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