Skip to main content
Glama
puran-water

Corrosion Engineering MCP Server

by puran-water

Corrosion Engineering MCP Server

Physics-Based Corrosion Rate Prediction via Model Context Protocol

License: MIT Python 3.12+ MCP Compatible Version 0.3.0 Tests Passing Tools


⚠️ DEVELOPMENT STATUS: This project is under active development and is not yet production-ready. APIs, interfaces, and functionality may change without notice. Use at your own risk for evaluation and testing purposes only. Not recommended for production deployments.

Overview

Corrosion Engineering MCP Server is a FastMCP-based toolkit that provides AI agents with access to physics-based corrosion engineering calculations, ranging from rapid handbook lookups to mechanistic electrochemical models with dual-tier pitting assessment.

Current Status: Version 0.3.0 (2025-12-15)

  • 348 tests passing (100% coverage)

  • 14 MCP tools with corrosion_ prefix

  • All critical bugs fixed

See IMPLEMENTATION_ROADMAP.md for implementation history.


Quick Start

Installation

# Clone repository
git clone git@github.com:puran-water/corrosion-engineering-mcp.git
cd corrosion-engineering-mcp

# Activate virtual environment
source ../venv312/bin/activate  # Linux/Mac
# OR
..\venv312\Scripts\activate     # Windows

# Install dependencies
pip install -r requirements.txt

# Run tests
pytest tests/test_phase3_pitting_integration.py -v

MCP Configuration

Add to Claude Desktop config (.mcp.json):

{
  "mcpServers": {
    "corrosion-engineering": {
      "command": "python",
      "args": ["/path/to/corrosion-engineering-mcp/server.py"]
    }
  }
}

Full configuration guide: MCP_CONFIGURATION.md


Phase 3 Highlights: Dual-Tier Pitting Assessment

What's New

Tier 1 + Tier 2 Pitting Assessment: Combines fast empirical screening (PREN/CPT) with mechanistic electrochemical assessment (E_pit vs E_mix).

Example (SS316 in seawater):

result = calculate_localized_corrosion(
    material="SS316",  # or 316L, 316, UNS S31600 (aliases supported)
    temperature_C=25.0,
    Cl_mg_L=19000.0,
    pH=8.0,
    dissolved_oxygen_mg_L=8.0,  # ← Enables Tier 2
)

# Tier 1 (PREN/CPT - always available)
print(result["pitting"]["susceptibility"])  # "critical" (T > CPT)
print(result["pitting"]["CPT_C"])            # 10.0°C
print(result["pitting"]["PREN"])             # 24.7

# Tier 2 (E_pit vs E_mix - requires DO)
print(result["pitting"]["electrochemical_risk"])  # "low" (E_mix << E_pit)
print(result["pitting"]["E_pit_VSCE"])            # 1.084 V_SCE
print(result["pitting"]["E_mix_VSCE"])            # 0.501 V_SCE

# Tier disagreement detection
print(result["tier_disagreement"]["detected"])    # True
print(result["tier_disagreement"]["explanation"]) # "Trust Tier 2 for accurate assessment..."

Key Insight: CPT test (ferric chloride, saturated) is conservative. Tier 2 provides mechanistic driving force (ΔE = E_mix - E_pit) accounting for actual dissolved oxygen.

Codex UX Improvements ✅

  1. Self-describing Tier 2 unavailability: electrochemical_interpretation explains WHY (not just null)

  2. RedoxState warnings surfaced: DO saturation, anaerobic conditions appended to interpretation

  3. Material alias mapping: 316L, 316, UNS S31600 → SS316 (6 aliases supported)

  4. Tier disagreement detection: Automatic warning when Tier 1 ≠ Tier 2 with guidance

Full user guide: docs/TIER1_VS_TIER2_PITTING_GUIDE.md


Architecture: 4-Tier Framework

Tier 0: Handbook Lookup (~0.5 sec)

Purpose: Rapid screening via semantic search on 2,980 vector chunks

Tools (3):

  • corrosion_screen_materials - Material-environment compatibility

  • corrosion_query_typical_rates - Empirical corrosion rates

  • corrosion_identify_mechanism - Mechanism identification + mitigation


Tier 1: Chemistry (~1 sec)

Purpose: Aqueous speciation and scaling indices

Tools (2):

  • corrosion_langelier_index - Water scaling tendency (Langelier SI)

  • corrosion_predict_scaling - Mineral scale prediction


Tier 2: Mechanistic Physics (1-5 sec)

Purpose: First-principles electrochemical models

Tools (7):

  • corrosion_predict_co2_h2s - NORSOK M-506 sweet/sour service

  • corrosion_predict_aerated_chloride - O₂ mass transfer limited

  • corrosion_assess_galvanic - NRL mixed-potential Butler-Volmer solver

  • corrosion_generate_pourbaix - E-pH stability diagrams

  • corrosion_get_material_properties - Alloy database (18+ materials)

  • corrosion_estimate_service_life - Remaining life prediction


Tier 3: Localized Corrosion

Purpose: Pitting/crevice assessment

Tools (2):

  • corrosion_assess_localized - Dual-tier pitting + crevice

    • Tier 1: PREN/CPT empirical (ASTM G48, ISO 18070)

    • Tier 2: E_pit vs E_mix mechanistic (NRL Butler-Volmer)

  • corrosion_calculate_pren - PREN calculation


Tool Count: 14 Tools

Tier

Tools

Count

Tier 0: Handbook

screen_materials, query_typical_rates, identify_mechanism

3

Tier 1: Chemistry

langelier_index, predict_scaling

2

Tier 2: Mechanistic

predict_co2_h2s, predict_aerated_chloride, assess_galvanic, generate_pourbaix, get_material_properties, estimate_service_life

6

Tier 3: Localized

assess_localized, calculate_pren

2

Info

get_server_info

1

Total

14

All tools have the corrosion_ prefix (e.g., corrosion_screen_materials).


Recent Updates

Version 0.3.0 (2025-12-15)

  • 14 MCP tools registered with corrosion_ prefix

  • CSV-backed material properties for 18+ alloys

  • All critical bug fixes from code review complete

  • 348 tests passing

Key Fixes (0.3.0)

  • Galvanic K constant corrected (A/cm² → mm/year)

  • ipy unit conversion fixed (in/y → mm/y)

  • DO scaling for user-supplied values

  • Zero-CO₂ short-circuit in NORSOK model

  • NORSOK pH clamping (prevents errors for pH > 6.5)


JSON Response Schema

All tools return standardized JSON with:

  • Central estimates (median or nominal values)

  • Uncertainty bounds (p05, p95 percentiles)

  • Provenance metadata (model, validation, sources, confidence)

Example (calculate_localized_corrosion):

{
  "pitting": {
    "CPT_C": 10.0,
    "PREN": 24.7,
    "Cl_threshold_mg_L": 233,
    "susceptibility": "critical",
    "margin_C": -15.0,
    "interpretation": "CRITICAL: T = 25.0°C exceeds CPT...",
    "E_pit_VSCE": 1.084,
    "E_mix_VSCE": 0.501,
    "electrochemical_margin_V": -0.583,
    "electrochemical_risk": "low",
    "electrochemical_interpretation": "LOW RISK: E_mix (0.501 V) is 583 mV below E_pit..."
  },
  "tier_disagreement": {
    "detected": true,
    "tier1_assessment": "critical",
    "tier2_assessment": "low",
    "explanation": "⚠️ TIER DISAGREEMENT: Tier 1 (PREN/CPT empirical) says 'critical' but Tier 2 (E_pit vs E_mix mechanistic) says 'low'. Recommendation: Trust Tier 2..."
  },
  "overall_risk": "critical",
  "recommendations": [
    "⚠️ TIER DISAGREEMENT: Trust Tier 2 for accurate assessment...",
    "CRITICAL: Immediate risk of localized corrosion..."
  ]
}

Validated Materials (Phase 3)

Material

Tier 1 (PREN/CPT)

Tier 2 (E_pit/E_mix)

Status

SS316

✅ Production-ready (seawater validated)

316L

✅ Alias → SS316

316

✅ Alias → SS316

UNS S31600/S31603

✅ Alias → SS316

HY80

⚠️

⚠️ Tier 1 only (negative ORR activation energy at seawater)

HY100

⏳ Tier 2 untested (coefficients available)

2205

✅ Tier 1 only (not in NRL DB, PREN=35)

254SMO

✅ Tier 1 only (not in NRL DB, PREN=43)


Directory Structure

corrosion-engineering-mcp/
├── server.py                           # FastMCP server
├── requirements.txt                    # Dependencies
├── .mcp.json                          # FastMCP configuration
├── README.md                           # This file
│
├── core/                               # Core architecture
│   ├── interfaces.py                   # Abstract base classes
│   ├── schemas.py                      # Pydantic models (CorrosionResult, etc.)
│   ├── state_container.py              # CorrosionContext caching
│   ├── phreeqc_adapter.py              # PHREEQC backend
│   └── localized_backend.py            # Tier 1+2 pitting backend (Phase 3)
│
├── tools/                              # MCP tool implementations
│   ├── handbook/                       # Tier 0: Semantic search
│   │   ├── material_screening.py       # ✅ screen_materials
│   │   ├── typical_rates.py            # ✅ query_typical_rates
│   │   └── mechanism_guidance.py       # ✅ identify_mechanism
│   ├── chemistry/                      # Tier 1: PHREEQC
│   │   ├── speciation.py               # ✅ run_phreeqc_speciation
│   │   └── calculate_pourbaix.py       # 🔄 calculate_pourbaix (stub)
│   └── mechanistic/                    # Tier 2: Physics models
│       ├── co2_h2s.py                  # ✅ predict_co2_h2s_corrosion
│       ├── aerated_chloride.py         # ✅ predict_aerated_chloride_corrosion
│       ├── predict_galvanic_corrosion.py # ✅ predict_galvanic_corrosion
│       └── localized_corrosion.py      # ✅ calculate_localized_corrosion (Phase 3)
│
├── utils/                              # Utility modules
│   ├── material_database.py            # Authoritative materials data
│   ├── nrl_materials.py                # NRL electrochemical database (Phase 3)
│   ├── nrl_constants.py                # Physical constants (Phase 3)
│   ├── nrl_electrochemical_reactions.py # ORR, HER, Fe oxidation (Phase 3)
│   ├── pitting_assessment.py           # E_pit calculator (Phase 3)
│   ├── redox_state.py                  # DO ↔ Eh conversion (Phase 3)
│   └── nacl_solution_chemistry.py      # NaCl solution properties (Phase 3)
│
├── external/                           # External data files
│   ├── nrl_coefficients/               # 23 CSV files (NRL Butler-Volmer)
│   └── nrl_matlab_reference/           # 12 MATLAB files (NRL validation)
│
├── tests/                              # Test suite
│   ├── test_phase3_pitting_integration.py  # ✅ 9/9 passing
│   ├── test_phase2_galvanic.py             # Phase 2 tests
│   └── test_redox_state.py                 # RedoxState module tests
│
└── docs/                               # Documentation
    ├── TIER1_VS_TIER2_PITTING_GUIDE.md     # User guide (600+ lines)
    ├── POURBAIX_PHREEQC_ROADMAP.md         # Pourbaix integration plan
    └── ...                                 # Additional documentation

Implementation Summary

Completed Features

  • Tier 0: Handbook lookup (semantic search on 2,980 chunks)

  • Tier 1: Chemistry (Langelier SI, scaling prediction)

  • Tier 2: Mechanistic physics (CO₂/H₂S, aerated chloride, galvanic, Pourbaix)

  • Tier 3: Localized corrosion (dual-tier pitting, PREN)

  • Materials: NRL database (6 alloys) + CSV database (18+ alloys)

Future Roadmap

  • Corrosion under insulation (CUI) prediction

  • Monte Carlo uncertainty quantification

  • Enhanced PHREEQC integration


Usage Examples

Example 1: Dual-Tier Pitting Assessment (Phase 3)

# SS316 in aerated seawater
result = calculate_localized_corrosion(
    material="316L",  # Alias → SS316
    temperature_C=25.0,
    Cl_mg_L=19000.0,
    pH=8.0,
    dissolved_oxygen_mg_L=8.0,  # Enables Tier 2
)

# Tier 1: Conservative CPT screening
print(f"CPT: {result['pitting']['CPT_C']}°C")             # 10.0
print(f"Tier 1 Risk: {result['pitting']['susceptibility']}")  # "critical"

# Tier 2: Mechanistic electrochemical
print(f"E_pit: {result['pitting']['E_pit_VSCE']:.3f} V_SCE")  # 1.084
print(f"E_mix: {result['pitting']['E_mix_VSCE']:.3f} V_SCE")  # 0.501
print(f"ΔE: {result['pitting']['electrochemical_margin_V']*1000:.0f} mV")  # -583
print(f"Tier 2 Risk: {result['pitting']['electrochemical_risk']}")  # "low"

# Disagreement detection
if result['tier_disagreement']['detected']:
    print(result['tier_disagreement']['explanation'])
    # "Trust Tier 2 for accurate assessment - it accounts for actual
    #  electrochemical driving force and redox conditions..."

Example 2: Galvanic Corrosion (Phase 2)

# Steel bolts in stainless flange
result = predict_galvanic_corrosion(
    anode_material="HY80",
    cathode_material="SS316",
    temperature_C=25.0,
    pH=7.5,
    chloride_mg_L=800.0,
    area_ratio_cathode_to_anode=50.0,  # Large flange, small bolts
)

print(f"Galvanic current density: {result['galvanic_current_density_A_cm2']:.2e} A/cm²")
print(f"Anode corrosion rate: {result['anode_corrosion_rate_mm_year']:.3f} mm/year")
if 'warnings' in result and result['warnings']:
    print(f"Warnings: {result['warnings']}")

Example 3: Handbook Screening (Tier 0)

result = screen_materials(
    environment="CO2-rich brine, 60°C, pCO2=0.5 bar, pH 6.8",
    candidates=["CS", "316L", "duplex_2205"],
    application="piping"
)

print(f"Material: {result.material}")
print(f"Compatibility: {result.compatibility}")
print(f"Typical rate range: {result.typical_rate_range} mm/y")

Design Decisions

Why Dual-Tier Pitting?

Decision: Combine empirical (PREN/CPT) with mechanistic (E_pit vs E_mix)

Rationale:

  • Tier 1 (PREN/CPT): Fast, conservative screening (worst-case ferric chloride test)

  • Tier 2 (E_pit vs E_mix): Mechanistic driving force accounting for redox state

  • Graceful degradation: Tier 2 optional (requires DO + NRL material), falls back to Tier 1

  • Codex validation: "Tier 1 is guaranteed, Tier 2 is opt-in... solid UX"

Why Material Aliases?

Decision: Map 316L, 316, UNS S31600 → SS316

Rationale (Codex recommendation):

  • Prevents "Tier 2 unavailable" frustration

  • Users don't need to memorize "SS316" vs "316L"

  • 6-line alias dict prevents 90% of support issues

Why Tier Disagreement Detection?

Decision: Automatic warning when Tier 1 ≠ Tier 2

Rationale (Codex recommendation):

  • "Expose the conflict explicitly" when tiers disagree

  • Clear guidance: "Trust Tier 2 for accurate assessment"

  • Example: SS316 seawater → Tier 1 "critical" vs Tier 2 "low" (Tier 2 correct)


Production Deployment

Pre-Deployment Checklist

  • All tests passing (9/9 Phase 3 integration tests)

  • Codex recommendations implemented (4/4)

  • Documentation complete (user guide, deployment checklist)

  • Known limitations documented (HY80 at seawater)

  • API backward compatible (100%)

Smoke Tests

See: PRODUCTION_DEPLOYMENT_CHECKLIST.md

Monitoring Metrics

  • Tier 2 availability rate (target: >50%)

  • Tier disagreement rate (expected: 20-40%)

  • Error rate (target: <5%)

  • Latency p95 (target: <2 seconds)


Dependencies

Current (requirements.txt):

# MCP Framework
fastmcp>=0.1.0

# Core
numpy>=1.24.0
pandas>=2.0.0
scipy>=1.10.0
pydantic>=2.0.0

# Chemistry
phreeqpython>=1.5.5

# Testing
pytest>=7.0.0
pytest-cov>=4.0.0
pytest-asyncio>=0.21.0

References

Authoritative Sources

  1. U.S. Naval Research Laboratory - Butler-Volmer electrochemical kinetics

    • Repository: USNavalResearchLaboratory/corrosion-modeling-applications (MIT)

  2. ASTM G48 - Critical Pitting Temperature tabulated data

  3. ISO 18070 / NORSOK M-506 - Chloride thresholds

  4. Garcia & Gordon (1992) - DO saturation model

Documentation


License

MIT License - See LICENSE file for details


Citation

@software{corrosion_mcp_2025,
  title={Corrosion Engineering MCP Server},
  author={Puran Water LLC},
  year={2025},
  url={https://github.com/puran-water/corrosion-engineering-mcp},
  version={0.3.0}
}

Version: 0.3.0 | Tests: 348 passing | Tools: 14

Last Updated: 2025-12-15

-
security - not tested
A
license - permissive license
-
quality - not tested

Resources

Looking for Admin?

Admins can modify the Dockerfile, update the server description, and track usage metrics. If you are the server author, to access the admin panel.

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/puran-water/corrosion-engineering-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server