Registry Review MCP Server

Version: 2.0.0 Status: Phase 4.2 Complete (LLM-Native Field Extraction + Performance Optimization) Next: Phase 5 (Integration & Polish)

Automated registry review workflows for carbon credit project registration using the Model Context Protocol (MCP).

Overview

The Registry Review MCP Server automates the manual document review process for carbon credit project registration. It transforms a 6-8 hour manual review into a 60-90 minute guided workflow with complete audit trail and structured outputs.

Core Value Proposition: Enable Registry Agents (like Becca) to process project documentation 5-10x faster by automating:

• Document discovery and classification

• Evidence extraction and requirement mapping

• Cross-document validation

• Compliance checking and report generation

Quick Start

Once integrated with Claude Desktop, try the complete workflow:

The prompts guide you through the entire review process automatically!

Features

✅ Phase 1: Foundation (Complete)

• Session Management - Create, load, update, and delete review sessions

• Atomic State Persistence - Thread-safe file operations with locking

• Configuration Management - Environment-based settings with validation

• Error Hierarchy - Comprehensive error types for graceful handling

• Caching Infrastructure - TTL-based caching for expensive operations

• Checklist System - Soil Carbon v1.2.2 methodology with 23 requirements

✅ Phase 2: Document Processing (Complete)

• Document Discovery - Recursively scan project directories for documents

• Smart Classification - Auto-classify documents by type (project plan, baseline report, monitoring report, etc.)

• PDF Text Extraction - Extract text and tables from PDF documents with caching

• GIS Metadata - Extract metadata from shapefiles and GeoJSON files

• Markdown Integration - Read markdown conversions from marker skill

• Quick-Start Workflow - Single-command session creation + discovery

• Auto-Selection - Prompts automatically select most recent session

• Helpful Guidance - Clear error messages with actionable next steps

✅ Phase 3: Evidence Extraction (Complete)

• Requirement Mapping - Automatically map all 23 checklist requirements to documents

• Evidence Extraction - Extract text snippets with ±100 words of context

• Page Citations - Include page numbers from PDF markers for precise references

• Section References - Add markdown section headers for navigation

• Keyword-Based Search - Smart keyword extraction with phrase detection and stop-word filtering

• Relevance Scoring - Documents scored 0.0-1.0 based on keyword coverage and density

• Status Classification - Automatic covered/partial/missing/flagged classification

• Coverage Analysis - Overall statistics with confidence scores (85-90% accuracy on test data)

• Structured Field Extraction - Extract specific data fields (dates, IDs) using regex patterns

✅ Phase 4: Validation & Reporting (Complete)

• Cross-Document Validation - Date alignment (120-day rule), land tenure (fuzzy matching), project ID consistency

• Validation Results - Status indicators (pass/warning/fail) with flagged items for review

• Report Generation - Markdown and JSON formats with complete findings and evidence

• Structured Output - Machine-readable reports with all evidence, validations, and citations

• Summary Statistics - Requirements coverage, validation results, items for human review

✅ Phase 4.2: LLM-Native Field Extraction (Complete)

• Intelligent Date Extraction - LLM-powered extraction of project dates with high accuracy (80%+ recall)

• Land Tenure Analysis - Owner name extraction with fuzzy deduplication (75% similarity threshold)

• Project ID Recognition - Automated extraction with false positive filtering

• Cost Optimization - Prompt caching (90% reduction), session fixtures (66% reduction), parallel processing

• Production-Ready Infrastructure - Retry logic, boundary-aware chunking, comprehensive validation

• Quality Assurance - 99 tests (100% passing) with real-world accuracy validation

📋 Phase 5: Planned

• Human review workflow and approval decision support

• Export to additional formats (PDF, CSV)

• Advanced contradiction detection

• Integration with external registry systems

Installation

Prerequisites

• Python >=3.10

• UV package manager

• 4GB RAM minimum (8GB recommended)

• 3GB disk space for ML models (marker PDF conversion)

• GPU optional but recommended for faster PDF conversion

Setup

First PDF Conversion: On first use, marker will download ML models (~1GB, one-time). This takes 30-60 seconds. Subsequent conversions are fast (models are cached).

System Requirements:

• CPU Mode: 10-30 seconds per PDF page (default)

• GPU Mode: 2-5 seconds per PDF page (with CUDA GPU)

• Storage: ~3GB for marker models + document storage

• Memory: 4GB minimum, 8GB recommended for large PDFs

Configuration

Environment Variables (.env)

The MCP server is configured via environment variables. Copy .env.example to .env and configure:

Required for LLM Extraction (Phase 4.2):

Optional Configuration:

See .env.example for all available configuration options.

Claude Desktop Integration

Add to claude_desktop_config.json:

Note: The server automatically loads configuration from .env in the project directory. You don't need to specify environment variables in claude_desktop_config.json unless you want to override them.

Restart Claude Desktop to load the server.

Usage

The Complete Workflow (3 Simple Prompts)

Stage 1: Initialize

Creates a new review session with project metadata.

Stage 2: Document Discovery

Discovers and classifies all documents (auto-selects your session).

Stage 3: Evidence Extraction

Maps all requirements to evidence with page citations (auto-selects your session).

That's it! Three prompts, fully automated, highly informative results.

Alternative: Provide Details to Any Stage

Each prompt can accept project details directly:

This creates a session and discovers documents in one step!

Available Tools

Session Management:

• start_review - Quick-start: Create session and discover documents in one step

• create_session - Create new review session

• load_session - Load existing session

• list_sessions - List all sessions

• delete_session - Delete a session

Document Processing:

• discover_documents - Scan and classify project documents

• extract_pdf_text - Extract text from PDF files

• extract_gis_metadata - Extract GIS shapefile metadata

Evidence Extraction:

• extract_evidence - Map all requirements to documents and extract evidence

• map_requirement - Map a single requirement to documents with evidence snippets

Validation:

• cross_validate - Run all cross-document validation checks

• validate_date_alignment - Verify dates are within 120-day rule

• validate_land_tenure - Check land tenure consistency with fuzzy name matching

• validate_project_id - Verify project ID patterns and consistency

Report Generation:

• generate_review_report - Generate complete review report in Markdown or JSON

• export_review - Export report to custom location

Prompts (Workflow Stages):

• /initialize - Stage 1: Create session and load checklist

• /document-discovery - Stage 2: Discover and classify documents

• /evidence-extraction - Stage 3: Extract evidence for all requirements

• /cross-validation - Stage 4: Run cross-document validation checks

• /report-generation - Stage 5: Generate complete review reports

Example Session

Development

Running Tests

Current Test Coverage:

• 99 total tests (100% passing)

• Phase 1 (Infrastructure): 23 tests

• Phase 2 (Document Processing): 6 tests

• Phase 3 (Evidence Extraction): 6 tests

• Phase 4 (Validation & Reporting): 19 tests

• Phase 4.2 (LLM Extraction): 32 tests

  • Unit tests (extraction, chunking, caching): 20 tests

  • JSON validation: 17 tests

  • Real-world accuracy tests: 3 tests

• Locking Mechanism: 4 tests

• UX Improvements: 3 tests

• Integration & Fixtures: 6 tests

Code Quality

Project Structure

The 7-Stage Workflow

1. ✅ Initialize - Create session and load checklist

2. ✅ Document Discovery - Scan and classify all documents

3. ✅ Evidence Extraction - Map requirements to evidence with page citations

4. ✅ Cross-Validation - Verify consistency across documents (dates, land tenure, project IDs)

5. ✅ Report Generation - Generate structured review report (Markdown + JSON)

6. 📋 Human Review - Present flagged items for decision (Phase 5)

7. 📋 Complete - Finalize and export report (Phase 5)

Roadmap

See ROADMAP.md for detailed implementation plan.

Current Status:

• ✅ Phase 1 (Foundation): Complete

• ✅ Phase 2 (Document Processing): Complete

• ✅ Phase 3 (Evidence Extraction): Complete

• ✅ Phase 4 (Validation & Reporting): Complete

• ✅ Phase 4.2 (LLM-Native Field Extraction): Complete

• 📋 Phase 5 (Integration & Polish): Next

Phase 4.2 Achievements:

Core Extraction Capabilities:

• LLM-powered field extraction for dates, land tenure, and project IDs

• Intelligent date parsing with 80%+ recall on real-world documents

• Owner name extraction with fuzzy deduplication (75% similarity threshold)

• Project ID recognition with false positive filtering

Performance Optimization (9 Refactoring Tasks Completed):

1. BaseExtractor Class - Eliminated 240 lines of duplicate code through inheritance

2. Configuration Documentation - Documented 6 LLM settings in .env.example

3. JSON Validation Tests - 17 comprehensive tests for malformed API responses

4. Retry Logic - Exponential backoff with jitter (1s → 32s max delay)

5. Parallel Processing - Concurrent chunk processing with asyncio.gather()

6. Fuzzy Deduplication - rapidfuzz integration for name variations

7. Boundary-Aware Chunking - Smart splitting at paragraphs/sentences/words

8. Prompt Caching - 90% cost reduction with Anthropic ephemeral cache

9. Integration Test Fixtures - 66% test cost reduction with session-scoped fixtures

Quality Assurance:

• 99 tests (100% passing) - up from 61 tests

• Real-world accuracy validation with Botany Farm ground truth

• Comprehensive JSON validation and error handling

• Cost tracking and API call monitoring

Performance Metrics:

• Full evidence extraction: ~2.4 seconds for 23 requirements

• Cross-validation: <1 second for all checks

• Report generation: ~0.5 seconds for both Markdown and JSON

• Coverage on Botany Farm: 73.9% (11 covered, 12 partial, 0 missing)

• Test execution: ~15 seconds for full suite (99 tests)

• API cost reduction: 90% via caching, 66% test cost reduction via fixtures

License

Copyright © 2025 Regen Network Development, Inc.

Last Updated: November 12, 2025 Next Milestone: Phase 5 - Integration, Human Review Workflow & Polish