I never expected to spend evenings debugging API clients for sheep genetics. But here’s the thing: good breeding data exists, locked behind interfaces designed for manual lookups. That bothered me.

The National Sheep Improvement Program (NSIP) maintains genetic evaluations for over 20 sheep breeds. Expected Breeding Values (EBVs), pedigrees spanning generations, progeny performance data. All accessible through a web interface that works fine if you’re checking one or two animals. If you’re managing a breeding program or comparing dozens of rams, you’re clicking through pages for hours.

I built nsip-api-client to fix that. It’s a Python library that wraps NSIP’s search functionality, handles the async requests, caches intelligently, and exposes the data through clean APIs. More importantly, it enabled something I hadn’t planned for: an MCP server that lets Claude reason about breeding decisions using real genetic data.

Why This Matters

Sheep breeding traditionally relied on visual assessment and production records. NSIP changed that by providing objective genetic merit scores. A ram with a Weaning Weight EBV of +5 lbs means his offspring will average 5 lbs heavier at weaning than the breed baseline, assuming equal management.

The problem isn’t the data quality. It’s accessibility. Small-scale breeders don’t have time to manually aggregate data across multiple animals, trace pedigrees to identify inbreeding risks, or compare rams from different flocks. The friction of access means good data goes underutilized.

Software can eliminate that friction. But it has to work reliably and handle the domain complexity.

Building the Python Client

The core library provides async access to NSIP’s database:

from nsip_api_client import NSIPClient

async with NSIPClient() as client:
    # Find Katahdin rams born after 2023
    results = await client.search_animals(
        breed="Katahdin",
        gender="Ram",
        min_birth_date="2023-01-01"
    )
    
    # Get complete pedigree (3 generations)
    lineage = await client.get_lineage(results[0].lpn_id, generations=3)
    
    # Retrieve all offspring
    progeny = await client.get_progeny(results[0].lpn_id)

The implementation uses aiohttp for concurrent requests and implements smart caching. Pedigree trees get cached because they don’t change. Search results expire after 6 hours. Animal details cache with a 24-hour TTL, balancing freshness with performance.

Error handling matters here. Network issues, rate limits, malformed responses: all get wrapped in specific exception types so calling code can handle failures appropriately.

from nsip_api_client.exceptions import (
    NSIPNotFoundError,
    NSIPConnectionError,
    NSIPTimeoutError
)

try:
    animal = await client.get_animal_details("123456")
except NSIPNotFoundError:
    # Animal doesn't exist in database
    handle_missing_animal()
except NSIPConnectionError:
    # Retry logic here
    schedule_retry()

The CLI wraps these APIs for quick terminal access:

# List breeds
nsip-search breeds

# Find top rams by trait
nsip-search animals --breed Katahdin --min-wwt 15

# Export to JSON
nsip-search animals --breed Katahdin --output rams.json

Why Rust Next

Python works well for this. Async performance is solid, the ecosystem has good HTTP and JSON libraries, and prototyping was fast. But I’m considering a Rust rewrite for several reasons:

Performance: Pedigree tree construction involves recursive lookups and graph building. Rust’s zero-cost abstractions would speed this up meaningfully.

Memory efficiency: Large datasets (full breed searches return 1000+ animals) consume significant memory in Python. Rust’s ownership model would let me control allocation precisely.

Distribution: A single binary with no runtime dependencies beats “install Python 3.11+, pip install, hope their environment works.”

Safety: Sheep breeding data has edge cases. Null fields, inconsistent dates, malformed pedigrees. Rust’s type system catches these at compile time instead of runtime.

The tradeoff: development time. Rust’s learning curve is real. For the current use case, Python’s advantages outweigh Rust’s. But as feature complexity grows (graph algorithms for optimal breeding paths, parallel pedigree analysis), Rust becomes more attractive.

I’m watching the Rust async ecosystem. When tokio and related crates stabilize further, a port makes sense.

GitHub Copilot Integration: The Vision

Here’s where this gets interesting. NSIP data is valuable, but interpreting it requires domain knowledge. An EBV is just a number without context about breed standards, trait correlations, and selection pressure tradeoffs.

GitHub Copilot integration through the MCP server changes the equation. Imagine working in a notebook or script and asking:

“Show me Katahdin rams with high parasite resistance but not at the expense of growth rate.”

The MCP server translates that to API calls, fetches data, filters by relevant EBVs (fecal egg count, weaning weight), and returns ranked results with explanations. Or:

“Calculate inbreeding coefficient if I breed ram A to ewe B.”

The server retrieves both pedigrees, walks the lineage trees, computes the coefficient, and explains the risk level.

This works because the Shepherd agent (built on the MCP server) combines NSIP data access with breeding knowledge. It knows which traits matter for different production goals. It understands regional disease pressure. It can recommend against breeding decisions that look good on paper but fail in practice.

# Via MCP, Claude can execute these directly
shepherd = ShepherdAgent()

# Natural language to structured query
results = shepherd.find_rams(
    breed="Katahdin",
    priorities=["parasite_resistance", "growth_rate"],
    budget=2500
)

# Risk analysis
risk = shepherd.assess_inbreeding(
    ram_id="123456",
    ewe_id="789012"
)

The agent handles the API complexity, caching, and domain interpretation. The user asks questions in plain language.

I’m gauging interest in this. The infrastructure exists. If breeders find it valuable, I’ll prioritize the GitHub interface and expand the agent’s capabilities. If not, the Python library and MCP server already provide the core value.

ROI Calculator: Making Purchase Decisions Data-Driven

Ram purchases represent significant capital investment. A proven breeder can cost $2,000 to $5,000. The question: does genetic merit justify the premium?

The ROI calculator uses EBVs to project value over the ram’s productive lifetime:

from nsip_skills import calculate_ram_roi

roi = calculate_ram_roi(
    ram_lpn="123456",
    purchase_price=2500,
    ewes_bred=50,
    years=4,
    lamb_price_per_lb=2.50
)

print(f"Projected value: ${roi.total_value}")
print(f"Payback period: {roi.payback_months} months")
print(f"ROI: {roi.return_percentage}%")

The calculation considers:

  • Weaning weight EBV (more pounds = more revenue per lamb)
  • Reproductive efficiency EBV (more lambs weaned per ewe)
  • Maternal ability (if breeding replacement ewes)
  • Expected productive lifespan

A ram with +8 lb weaning weight EBV breeding 50 ewes annually generates tangible economic value. If each lamb weighs 8 lbs more at market (at $2.50/lb), that’s $20 per lamb, $1,000 per year for 50 lambs, $4,000 over 4 years. The $2,500 purchase price makes sense.

But a ram with mediocre EBVs at the same price? The numbers don’t work. The calculator makes this explicit.

Visualizing Genetic Performance

Numbers on spreadsheets don’t communicate effectively. Charts do.

The visualization tools generate plots showing:

Trait distributions: Where does this ram rank within the breed for key traits?

Pedigree trees: Visual representation of lineage, with EBVs color-coded by performance.

Progeny performance: Boxplots comparing offspring to breed average across traits.

Selection pressure: Radar charts showing trait balance (growth vs. efficiency vs. hardiness).

from nsip_skills import plot_trait_spider

# Generate radar chart
plot_trait_spider(
    ram_lpn="123456",
    traits=["wwt", "pwt", "nwt", "emda", "fleece_weight"],
    output="ram_profile.png"
)

These visuals help identify well-rounded genetics vs. single-trait outliers. A ram with exceptional growth but poor maternal transmission might work for terminal crosses but not for breeding replacements. The chart makes that obvious at a glance.

Breeding Plans: Optimizing Matings

Breeding season planning involves matching rams to ewes to maximize genetic gain while managing inbreeding. With 200 ewes and 8 rams, the permutations are overwhelming to calculate manually.

The breeding plan optimizer uses linear programming to suggest optimal pairings:

from nsip_skills import optimize_breeding_plan

plan = optimize_breeding_plan(
    ewes=ewe_list,  # List of ewe LPN IDs
    rams=ram_list,  # List of ram LPN IDs
    objectives=["maximize_wwt", "minimize_inbreeding"],
    constraints={"max_inbreeding_coefficient": 0.0625}
)

for assignment in plan.matings:
    print(f"Breed ewe {assignment.ewe_id} to ram {assignment.ram_id}")
    print(f"  Expected progeny WWT: +{assignment.projected_wwt} lbs")
    print(f"  Inbreeding coefficient: {assignment.inbreeding_coef}")

The optimizer balances competing objectives. Maximum genetic gain might suggest breeding top ram to 50 ewes, but that concentrates pedigree and increases future inbreeding risk. The algorithm finds the equilibrium.

This isn’t theoretical. I’ve tested it with real flock data. The plans are implementable and produce measurable gains.

The Shepherd Agent: Expert System for Breeding

The Shepherd agent combines NSIP data access with domain expertise encoded as rules and heuristics:

Breed recommendations: “I’m in Georgia raising meat sheep. Which breed fits my climate and market?”

Culling decisions: “Should I keep ewe 789012 for another breeding season?”

Disease management: “What parasites should I monitor in the Southeast, and when?”

Nutrition planning: “What feed protocol supports growth without excess finishing?”

The agent doesn’t just retrieve data. It interprets context:

User: "I bought ram 123456. What should I know?"

Shepherd: "Ram 123456 is a Katahdin with strong growth (+12 lb WWT EBV, 
90th percentile) but moderate parasite resistance (FEC EBV +200, 45th 
percentile). Given your location in Alabama:

- Expect fast-growing lambs but monitor fecal egg counts closely
- Deworm strategic offspring at 60 days
- Consider breeding to ewes with strong parasite resistance to balance
- His sire has good longevity; expect 5-6 productive years

Projected ROI at $2,200 purchase price: 23% over 5 years with 40 ewes."

That’s valuable. It’s not just data retrieval; it’s advice synthesized from genetics, environment, and production goals.

The agent runs locally via the MCP server or through GitHub issues for question-answer workflows. I’m testing the GitHub interface now to see if breeders prefer conversational interaction over programmatic APIs.

Real-World Impact

I’ve used these tools to evaluate ram purchases for my own flock. Last season, I compared three rams in the $2,000-$2,500 range. The EBVs were similar, but pedigree analysis revealed one had significant line breeding that would limit future mating options. Another had impressive growth but poor-producing daughters. The third balanced growth, maternal ability, and genetic diversity.

The ROI calculator confirmed the third ram’s value. His offspring have averaged 6 lbs heavier at weaning than the flock baseline. That’s 300 lbs additional revenue per year at current lamb prices, covering his cost in under two years.

Good tools enable better decisions. That’s the point.

Open Questions

Several areas need user feedback:

Rust rewrite timing: Is Python performance limiting anyone? The async implementation handles typical use cases well, but large-scale operations (processing entire breed datasets) could benefit from Rust.

GitHub Copilot interface: Would breeders use a conversational agent through GitHub issues, or do they prefer programmatic APIs and CLIs? The MCP server supports both, but development effort should focus on the preferred workflow.

Visualization preferences: What charts actually get used? I built what I thought was useful, but user behavior might reveal different priorities.

Feature requests: What analysis or decision support would be most valuable? Trait prediction for specific crosses? Feed efficiency calculators? Market price projections?

I’m interested in this feedback. If the tools solve real problems, I’ll continue expanding them. If they’re only marginally useful, the core library still provides value and I’ll maintain that.

Technical Details

For developers interested in the implementation:

Architecture: Async Python 3.11+, aiohttp for HTTP, pydantic for data validation, redis optional for distributed caching.

Testing: 95% coverage, integration tests hit live NSIP endpoints (with caching to avoid load), property-based testing for pedigree algorithms.

Distribution: PyPI for the client library, MCP server installable via uvx nsip-mcp-server, Docker image available for containerized deployments.

Documentation: Full API docs at GitHub, usage examples in the docs/examples directory, MCP server protocol details in the README.

Contributing: Open to PRs. The codebase is clean, tested, and documented. If you see improvements or want to add features, submit a PR or open an issue.

Conclusion

Software can solve problems outside traditional tech domains. Agriculture has data infrastructure (NSIP, genomic databases, IoT sensors) but often lacks the software to make that data actionable.

The nsip-api-client and Shepherd agent represent one approach: meet users where they are (CLI, Python APIs, conversational AI), reduce friction to accessing valuable data, and provide decision support that respects domain complexity.

I’ll continue developing these tools as long as they’re useful. The foundation is solid. The potential applications (optimizing breeding programs, predicting market outcomes, managing genetic diversity across breeds) are significant.

If you’re working in agriculture tech, breeding programs, or just interested in how software applies to non-traditional domains, check out the project. Feedback welcome.

Interested in the NSIP tools? The code is open source: nsip-api-client on GitHub. Install via PyPI: pip install nsip-client. Try the MCP server: uvx nsip-mcp-server. Questions or feature requests: open an issue or reach out on GitHub.