Gdal Mcp

What is Gdal Mcp about?

Gdal Mcp is a Model Context Protocol (MCP) server that wraps GDAL‑style geospatial libraries (Rasterio, GeoPandas, PyProj, etc.) to expose raster and vector operations to AI agents. Before executing a tool, the server requires the agent to justify choices such as CRS, resampling method, or query extent, creating an audit trail of reasoning.

How to use Gdal Mcp?

1. Install the package from PyPI (recommended via uvx):

   uvx --from gdal-mcp gdal --transport stdio
   

2. Configure the MCP client (e.g., Claude Desktop) to point to the gdal-mcp server, setting the GDAL_MCP_WORKSPACES environment variable to the folder containing your geospatial data.
3. Invoke tools through the MCP context API, e.g., raster_reproject, vector_clip, or any *_info request. The first call triggers a reflection dialogue; subsequent calls reuse cached justifications.

Key features of Gdal Mcp

• Reflection Middleware: Structured pre‑execution reasoning (intent, alternatives, trade‑offs, confidence) for CRS selection, resampling, and query extents.
• Cross‑Domain Caching: Justifications are shared between raster and vector tools, achieving ~75% cache‑hit rates.
• Full Tool Parity: 13 production‑ready tools covering raster info, conversion, reprojection, statistics, queries, and equivalent vector operations.
• Python‑Native Stack: Rasterio, PyProj, pyogrio, Shapely, NumPy, and Pydantic provide high performance and type safety.
• Security & Isolation: Workspace path validation and strict type checking.
• Extensive Documentation: Quick‑start guide, tools reference, environment variable list, and roadmap.

Use cases of Gdal Mcp

• Automated map production: AI agents generate web‑ready tiles, justifying projection and resampling choices.
• Remote sensing analysis pipelines: Chain DEM reprojection, slope calculation, and vector overlay with documented methodology.
• Educational AI assistants: Teach users why a specific CRS or interpolation method is appropriate, while preserving reproducibility.
• Audit‑ready geospatial workflows: Capture methodological decisions for regulatory compliance or scientific publishing.

FAQ from the Gdal Mcp

Q: Do I need to write Python code to call the tools? A: No. Interactions occur via the MCP protocol; the client (e.g., Claude, LangChain, custom agents) sends JSON requests and receives structured responses.

Q: What happens if the AI proposes an unsupported operation? A: The reflection middleware intercepts the request, prompts the agent to choose a supported alternative, and records the decision.

Q: Can I disable the reflection step? A: The system is designed to always capture justification; however, cached justifications make subsequent calls instantaneous, effectively bypassing repeated prompts.

Q: How are large datasets handled? A: Tools operate on file paths within defined workspaces; streaming and VRT support are planned for future releases (Phase 3).

Q: Is the server compatible with other MCP implementations? A: Yes. It follows the FastMCP 2.0 specification, allowing seamless integration with any compliant client.

GDAL MCP

Geospatial AI with epistemic reasoning

GDAL MCP is a Model Context Protocol (MCP) server that provides AI agents with geospatial analysis capabilities while requiring them to justify their methodological choices through a reflection middleware system.

🎉 v1.1.1 Released (2025-10-26) — Vector tool parity + cross-domain reflection validated
🧠 Reflection System — Domain-based epistemic reasoning that transcends data types
⚡ 75% Cache Hit Rate — Methodology reasoning carries across raster ↔ vector operations

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📚 Documentation

• Quick Start - Installation, setup, and MCP configuration
• Tools Reference - Complete tool documentation with examples
• Environment Variables - Runtime configuration and tool surface controls
• Vision - Long-term roadmap and philosophy
• Changelog - Release history and updates

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🧠 The Reflection System

What Makes GDAL MCP Different?

Most AI tool systems execute operations immediately when requested. GDAL MCP requires the AI to justify methodological decisions before execution, creating a conversation about the "why" rather than just executing the "what."

Traditional AI tool approach:

User: "Reproject this DEM to Web Mercator"
AI: *silently executes operation*
Result: DEM reprojected (but with what resampling? why that CRS? what tradeoffs?)

GDAL MCP with reflection:

User: "Reproject this DEM to Web Mercator for my web map"
AI: *reflection middleware intercepts*
AI: "Before reprojecting to EPSG:3857, let me justify this CRS choice:
     
     Intent: Web Mercator preserves angles (conformal projection),
             essential for web tile rendering and navigation
     
     Alternatives considered:
     - EPSG:4326 (WGS84) → rejected: not conformal, distorts shapes
     - Local UTM → rejected: limited extent, not web-standard
     
     Tradeoffs: Area distortion increases at high latitudes
                (acceptable for web visualization)
     
     Confidence: High - this is the standard for web mapping"

*justification stored, operation proceeds*
Result: DEM reprojected with documented methodology

Why This Matters

1. Utilizes Full MCP Protocol Capabilities

The Model Context Protocol isn't just about tool execution—it's about agentic reasoning and interaction. The reflection system leverages MCP's design to enable:

• Pre-execution prompting (tool dependencies)
• Structured reasoning (schema-validated justifications)
• Stateful workflows (justification caching)
• Human-in-the-loop interaction (advisory prompts)

2. Prevents Silent Failures

Geospatial operations can execute successfully while producing methodologically incorrect results:

• Nearest-neighbor resampling on continuous elevation data (creates artifacts)
• Web Mercator for area calculations (40%+ distortion possible)
• Bilinear interpolation on categorical data (creates invalid class values)

The reflection system surfaces these choices for validation.

3. Educational, Not Restrictive

The AI isn't blocked from executing operations—it's required to demonstrate understanding:

• First use: Explains reasoning, teaches methodology
• Cached: Instant execution (knowledge persists)
• Result: 75%+ cache hit rates, minimal friction

4. Creates Audit Trail

Every methodological decision is documented with:

• Intent (what property must be preserved?)
• Alternatives (what else was considered?)
• Rationale (why this choice?)
• Tradeoffs (what are the limitations?)
• Confidence (high/medium/low)

This enables reproducible geospatial science.

🎯 Example Workflow

Multi-Operation Geospatial Analysis

User: "I need to reproject this DEM to UTM for accurate slope analysis,
       then reproject this vector layer to the same CRS for overlay"

AI Workflow:
1. Inspects DEM metadata (raster_info)
2. REFLECTION: Justifies UTM Zone 10N choice (accurate distance/area)
3. REFLECTION: Justifies cubic resampling (smooth gradients for derivatives)
4. Reprojects DEM (raster_reproject)
5. Inspects vector metadata (vector_info)
6. CACHE HIT: Reuses UTM justification (cross-domain!)
7. Reprojects vector (vector_reproject) - instant, no re-prompting
8. Both datasets now aligned in UTM Zone 10N

Result: 2 operations, 2 reflections (not 3!)
Cache hit rate: 50% → Saves time, maintains methodology

The Key Innovation: The CRS justification from step 2 is reused in step 6 because the methodology (why UTM Zone 10N?) is domain-based, not tool-based. It doesn't matter if you're working with raster or vector data—the projection choice reasoning is the same.

See Tools Reference for detailed examples of all available tools.

⚡ Key Features

🧠 Reflection Middleware

• Pre-execution reasoning for CRS selection, resampling methods
• Structured justifications (intent, alternatives, choice, tradeoffs, confidence)
• Persistent cache with 75% hit rates in multi-operation workflows
• Cross-domain cache sharing - CRS justification works for both raster AND vector

🛠️ Comprehensive Toolset

• Raster tools: info, convert, reproject, stats, query
• Vector tools: info, reproject, convert, clip, buffer, simplify, query
• See Tools Reference for complete documentation

🛡️ Production Quality

• Full type safety (mypy strict mode)
• 72 passing tests
• Workspace security (path validation middleware)
• Python-native (Rasterio/PyProj/pyogrio)
• Real-time feedback via FastMCP Context API

📚 MCP Resources

• Workspace catalog for autonomous file discovery
• Metadata intelligence for format detection
• Reference knowledge base (CRS, resampling methods, compression options)
• Query result resources for inspecting spatial query outputs (query://result/{id})

📦 Quick Start

Install via uvx (Recommended)

# Run directly from PyPI
uvx --from gdal-mcp gdal --transport stdio

MCP Configuration (Claude Desktop)

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "gdal-mcp": {
      "command": "uvx",
      "args": ["--from", "gdal-mcp", "gdal", "--transport", "stdio"],
      "env": {
        "GDAL_MCP_WORKSPACES": "/path/to/your/geospatial/data"
      }
    }
  }
}

See QUICKSTART.md for:

• Alternative installation methods (Docker, local development)
• Detailed MCP client configuration
• Workspace security setup
• Troubleshooting guide

See docs/ENVIRONMENT_VARIABLES.md for all runtime flags including workspace scoping, tool registration toggles (RASTER/VECTOR), and query registry TTL/capacity.

🔧 Available Tools

GDAL MCP provides 13 production-ready tools across three categories:

Raster Operations

• raster_info - Inspect metadata (CRS, resolution, bands, nodata)
• raster_convert - Format conversion with compression & overviews (COG support)
• raster_reproject ⚡ - CRS transformation (with reflection)
• raster_stats - Statistical analysis with histograms
• raster_query ⚡ - Spatial window query (bbox or geometry)

Vector Operations

• vector_info - Inspect metadata (CRS, geometry, attributes)
• vector_reproject ⚡ - CRS transformation (with reflection)
• vector_convert - Format migration (SHP ↔ GPKG ↔ GeoJSON)
• vector_clip - Spatial subsetting
• vector_buffer - Proximity analysis
• vector_simplify - Geometry simplification
• vector_query ⚡ - Spatial/attribute query (bbox or geometry)

Reflection System

• store_justification - Cache epistemic reasoning (used internally)
• Advisory prompts for CRS selection, resampling methods, and query extents

⚡ = Reflection-enabled: These tools require methodological justification on first use, then cache for instant subsequent execution.

See TOOLS.md for complete documentation with examples and parameters.

Note: Spatial query tools currently provide core querying only (Phase 3a). Indexing/VRT optimizations are deferred to future phases.

🧪 Testing

# Run all tests
uv run pytest test/ -v

# With coverage
uv run pytest test/ --cov=src --cov-report=term-missing

Status: ✅ 72 passing tests including reflection system integration

🏗️ Architecture

Python-Native Stack (ADR-0017):

• Rasterio - Raster I/O and manipulation
• PyProj - CRS operations and transformations
• pyogrio - High-performance vector I/O (fiona fallback)
• Shapely - Geometry operations
• NumPy - Array operations and statistics
• Pydantic - Type-safe models with JSON schema

Key Design Decisions (26 ADRs guide development):

• ADR-0026: Reflection system and epistemic governance
• ADR-0017: Python-native over CLI shelling for performance
• ADR-0011: Explicit resampling required (prevents silent data corruption)
• ADR-0022: Workspace isolation for security

🤝 Contributing

We welcome contributions! See CONTRIBUTING.md for:

• Development setup
• Code style guide (Ruff + mypy)
• Testing requirements (pytest + fixtures)
• ADR process

📝 License

MIT License - see LICENSE for details.

🙏 Acknowledgments

• Built with FastMCP
• Powered by Rasterio and GDAL
• Inspired by the Model Context Protocol

🗺️ Roadmap

Current Status: v1.1.1 - Phase 2 Complete ✅

• Reflection middleware operational
• Vector/raster tool parity achieved
• Cross-domain cache sharing validated (75% hit rates)

Next: Phase 3 - Workflow Intelligence (v2.0+)

• Formal workflow composition
• Multi-step orchestration
• Analysis pattern libraries

See Vision for the complete long-term roadmap.

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Built with ❤️ for the geospatial AI community

Geospatial operations that think, not just execute.