Registry Review MCP Server

MCP server that automates carbon credit project registration reviews through an eight-stage workflow.

Overview

The Registry Review MCP Server transforms a 6-8 hour manual document review into a guided workflow where AI handles document organization, data extraction, and consistency checking while humans provide expertise, judgment, and final approval.

Core Capabilities:

• Document discovery and intelligent classification

• Requirement mapping with semantic matching

• Evidence extraction with page citations

• Cross-document validation (dates, land tenure, project IDs)

• Structured report generation (Markdown + JSON)

Quick Start

Claude Desktop Integration

Add to your claude_desktop_config.json:

The Eight-Stage Workflow

Each stage produces artifacts for human verification before proceeding. The workflow follows a collaboration model where AI handles tedious document processing while humans provide expertise and final judgment.

Stage A: Initialize

Create a review session with project metadata and load the checklist template.

Output: Session ID, project metadata, loaded checklist (23 requirements for Soil Carbon v1.2.2)

Stage B: Document Discovery

Scan the documents directory, extract file metadata, and classify each document by type.

Agent Actions:

• Recursively scan for PDFs, shapefiles, GeoJSON, spreadsheets

• Classify documents (project plan, baseline report, monitoring report, land tenure, etc.)

• Generate document inventory with confidence scores

Human Actions: Review classifications, mark documents as in-scope/ignored/pinned

Output: Document inventory with normalized names, types, and source references

Stage C: Requirement Mapping

Connect discovered documents to specific checklist requirements using semantic matching.

Agent Actions:

• Parse checklist into structured requirements with expected evidence types

• Analyze documents and suggest requirement → document mappings

• Flag requirements with no plausible matches

Human Actions: Confirm/reject suggested mappings, manually add missing mappings

Output: Mapping matrix linking each requirement to 0+ documents with confidence scores

Stage D: Evidence Extraction

Extract key data points and text snippets from mapped documents.

Agent Actions:

• Parse document content (PDF text, tables, metadata)

• Extract 0-3 evidence snippets per requirement with page citations

• Extract structured data: dates, locations, ownership info, numerical values

Human Actions: Review snippets, delete irrelevant ones, add manual notes

Output: Evidence database with snippets, citations, and structured data points

Stage E: Cross-Validation

Verify consistency, completeness, and compliance across all extracted evidence.

Validation Checks:

• Date Alignment: Sampling dates within ±120 days of imagery dates

• Land Tenure: Owner names consistent across documents (fuzzy matching)

• Project ID: Consistent project identifiers across all documents

• Completeness: Each requirement has mapped documents with sufficient evidence

Output: Validation results with pass/warning/fail flags and coverage statistics

Stage F: Report Generation

Produce structured, auditable Registry Review Report.

Output Formats:

• Markdown: Human-readable report with executive summary, per-requirement findings, citations

• JSON: Machine-readable for audit trails and downstream systems

Report Contents: Project metadata, coverage statistics, requirement findings with evidence snippets, validation results, items requiring human review

Stage G: Human Review

Expert validation, annotation, and revision handling.

Human Actions:

• Review flagged items requiring judgment

• Override agent assessments where expert knowledge differs

• Request revisions from proponent if gaps identified

• Make final determination: Approve / Conditional / Reject / On Hold

Output: Finalized report with human annotations and approval decision

Stage H: Completion

Finalize and archive the review.

Agent Actions:

• Lock finalized report

• Generate archive package with audit trail

• Prepare data for on-chain registration (if approved)

Output: Locked report, complete audit trail, archived session

Quick Example

Each stage auto-selects the most recent session, so you can run them in sequence without specifying session IDs.

Available Tools

Session Management:

• create_session - Create new review session

• load_session / list_sessions / delete_session - Session lifecycle

• start_review - Quick-start: create session + discover documents

• list_example_projects - List available test projects

File Upload:

• create_session_from_uploads - Create session from uploaded files

• upload_additional_files - Add files to existing session

• start_review_from_uploads - Full workflow from uploads

Document Processing:

• discover_documents - Scan and classify project documents

• add_documents - Add document sources to session

• extract_pdf_text - Extract text from PDFs

• extract_gis_metadata - Extract GIS shapefile metadata

Requirement Mapping:

• map_all_requirements - Semantic mapping to documents

• confirm_mapping / remove_mapping - Manual mapping adjustments

• get_mapping_status - View mapping statistics

Evidence & Validation:

• extract_evidence - Extract evidence for all requirements

• map_requirement - Map and extract for single requirement

Configuration

Copy .env.example to .env and configure:

See .env.example for all configuration options including chunking, image processing, cost management, and validation settings.

Project Structure

Development

Test Markers:

• smoke - Critical path tests (<1s)

• expensive - Tests with API costs (excluded by default)

• marker - PDF extraction tests (slow, 8GB+ RAM)

• accuracy - Ground truth validation tests

See pytest.ini for marker configuration.

Requirements

• Python >= 3.10

• uv package manager

• 4GB RAM minimum (8GB recommended for large PDFs)

License

Copyright 2025 Regen Network Development, Inc.