GDAL MCP

# GDAL MCP Vision **Status**: Living Document **Last Updated**: 2025-10-26 **Purpose**: Define the long-term vision and guiding principles for gdal-mcp development --- ## Executive Summary **gdal-mcp is not a drop-in replacement for GDAL commands.** It is a foundation for **agentic geospatial reasoning** - enabling AI agents to understand spatial problems, compose analytical workflows, and bridge the gap between domain expertise and technical implementation. --- ## The Problem We're Solving ### The Current Reality Geospatial analysis requires two forms of expertise that rarely coexist in one person: 1. **Domain Knowledge**: Understanding terrain, hydrology, ecology, geology, urban planning, etc. 2. **Technical Implementation**: GDAL syntax, projection systems, algorithm parameters, data formats, etc. **This creates barriers:** - A 30-year veteran hydrologist who deeply understands watersheds cannot perform basic slope analysis because they don't know GDAL syntax - A GIS analyst spends hours on Stack Overflow translating "what I want to do" into "the exact command to do it" - Domain experts are blocked by technical complexity, not lack of understanding - Knowledge remains siloed: experts can't operationalize their insights without developers ### Why This Matters **Geospatial expertise is being wasted.** People who understand the science cannot apply their knowledge because the tools require a different kind of expertise entirely. --- ## The Vision ### What Success Looks Like **In 5 years, a domain expert should be able to:** > "Show me all areas where slope exceeds 30 degrees within 500 meters of perennial streams, excluding urban zones, and calculate the erosion risk for each parcel." **And receive:** - A complete analysis workflow executed correctly - Results with spatial context - Explanation of methodology - Ability to refine and iterate in natural language **No GDAL commands. No Python scripts. No Stack Overflow.** Just domain expertise expressed in domain language, translated to precise geospatial operations by an AI agent that understands both. --- ## Core Principles ### 1. Agentic Reasoning Over Command Translation **Not This:** ``` User: "Reproject to Web Mercator" AI: Executes gdalwarp with EPSG:3857 ``` **This:** ``` User: "I need to display this elevation model in a web map" AI: Reasons about requirements → Web display needs Web Mercator → Elevation models need appropriate resampling → Executes reprojection with cubic resampling → Suggests COG format for web performance → Explains why these choices were made ``` **The AI should understand *why*, not just *what*.** ### 2. Multi-Step Workflow Composition **Not This:** - Single tool calls in isolation - User manually chains operations - No context between steps **This:** - AI plans entire workflows - Understands data flow between steps - Optimizes processing order - Handles intermediate results - Adapts based on data characteristics **Example:** ``` User: "Identify potential landslide zones in this region" AI Reasoning: 1. Understand requirement: slope analysis + hydrology 2. Check available data: DEM, stream network, soil maps 3. Plan workflow: - Extract slope from DEM - Identify steep areas (>30°) - Calculate distance to streams - Overlay with soil permeability - Combine factors into risk score 4. Execute pipeline 5. Return spatially-indexed results 6. Explain methodology and assumptions ``` ### 3. Domain Language, Not Technical Syntax **Domain experts speak in:** - Watersheds, not polygons - Slope, not derivatives - Riparian zones, not buffers - Erosion potential, not algorithms **The AI must:** - Understand domain terminology - Translate to technical operations - Preserve semantic intent - Explain in domain terms ### 4. Accessibility Without Compromise **Target Users:** - Hydrologists who understand watersheds but not programming - Ecologists who understand habitat but not GIS - Geologists who understand terrain but not GDAL - Urban planners who understand zoning but not spatial analysis - Field researchers who collect data but can't process it **These users have:** - Deep domain expertise (decades) - Limited technical background - Real analytical needs - No time to learn GDAL **They should not be limited by technical barriers.** --- ## Phased Implementation ### Phase 1: Foundation ✅ Complete (v0.1.0) **Goal**: Establish reliable base operations - Core GDAL tools (info, convert, reproject, stats) - Robust error handling - Production-ready infrastructure - MCP protocol compliance - Security (workspace scoping) **Status**: ✅ Complete ### Phase 2: Epistemic Middleware & Tool Parity ✅ Complete (v1.0.0 - v1.1.1) **Goal**: Build epistemic reasoning layer and comprehensive tool coverage **Completed**: - ✅ **Reflection middleware system** (v1.0.0) - Pre-execution justification for CRS, resampling, hydrology, aggregation - Domain-based cache sharing (validated v1.1.1) - Advisory prompting that respects user autonomy - ✅ **Complete vector tool suite** (v1.1.1) - Vector operations (reproject, convert, clip, buffer, simplify, info) - Feature parity with raster capabilities - ✅ **Cross-domain reflection** (v1.1.1) - Methodological reasoning transcends data types - 75% cache hit rate in multi-operation workflows - ✅ Comprehensive format conversions - ✅ Multi-band operations - ✅ Metadata management **Key Innovation**: Domain-based (not tool-based) epistemic reasoning. A CRS justification works for both raster and vector operations because the methodology is about the projection's properties, not the data type. **Focus**: Building the toolkit with methodological guardrails **Current Status**: v1.1.1 (2025-10-26) ### Phase 3: Workflow Intelligence (v2.0 - In Planning) **Goal**: Enable multi-step reasoning and composition **Planned**: - Workflow planning capabilities - Inter-tool data flow orchestration - Intermediate result management - Progress tracking for long operations - Context-aware tool selection - Composition pattern discovery **Foundation Ready**: The reflection cache already enables workflow context (justifications carry across operations). Phase 3 will formalize this into explicit workflow management. **Focus**: Moving from tools to workflows ### Phase 4: Domain Understanding (v2.x+) **Goal**: Agentic geospatial reasoning - Domain concept recognition (watersheds, viewsheds, etc.) - Methodology libraries (standard analytical workflows) - Adaptive algorithms (choose methods based on data) - Explanation generation (why these steps?) - Quality assessment (validate results) **Focus**: AI that understands geospatial science ### Phase 5: Collaborative Intelligence (v3.x+) **Goal**: Human-AI partnership for complex analysis - Interactive refinement - "What if" scenario modeling - Automated quality checks - Knowledge capture (learn from domain experts) - Workflow templates from successful analyses **Focus**: Amplifying human expertise with AI reasoning --- ## Design Implications ### What This Vision Means for Development 1. **Tool Design** - Tools must be composable (output → input chains) - Metadata must flow through pipelines - Context must be preservable - Operations must be explainable 2. **API Design** - Expose capabilities, not commands - Support workflow description - Enable reasoning about data characteristics - Provide semantic context 3. **Architecture** - Plan for stateful workflows - Support async long-running operations - Enable intermediate result inspection - Facilitate context sharing 4. **Documentation** - Emphasize *when* and *why*, not just *how* - Provide domain-oriented examples - Explain spatial concepts - Guide AI reasoning --- ## Success Metrics **We know we're succeeding when:** 1. **Domain experts can work independently** - Hydrologist analyzes watersheds without GIS staff - Ecologist maps habitat without developers - Geologist models terrain without Stack Overflow 2. **Complex analyses become conversations** - Multi-step workflows described in sentences - Iterative refinement through dialogue - No manual scripting required 3. **Quality increases** - AI chooses appropriate methods - Spatial relationships preserved - Results are reproducible - Methodology is documented 4. **Knowledge is captured** - Expert workflows become templates - Best practices encoded - Institutional knowledge preserved --- ## What This Is Not ### We Are Not Building: ❌ **QGIS in chat form** - We're enabling reasoning, not replicating GUIs ❌ **GDAL command generator** - We're composing workflows, not translating syntax ❌ **Black-box magic** - We're making expertise accessible, not hiding complexity ❌ **Replacement for domain knowledge** - We're amplifying expertise, not replacing it ### We Are Building: ✅ **A bridge** between domain expertise and technical implementation ✅ **An amplifier** for geospatial knowledge that's currently trapped ✅ **A foundation** for AI that understands spatial reasoning ✅ **A democratizer** making powerful analysis accessible to experts --- ## Guiding Questions for Development When evaluating features, ask: 1. **Does this enable reasoning or just execution?** - Good: "AI can understand why this operation is needed" - Bad: "AI can execute this command faster" 2. **Does this help domain experts or technical users?** - Good: "A hydrologist can express this in domain terms" - Bad: "A developer can write this command easier" 3. **Does this compose or isolate?** - Good: "This tool connects to other operations naturally" - Bad: "This tool only works in isolation" 4. **Does this explain or obscure?** - Good: "Users understand what happened and why" - Bad: "It works but users don't know how" --- ## Call to Action **This vision will take years to realize.** It requires: - Incremental, thoughtful development - Partnership with domain experts - Commitment to accessibility - Patience with complexity - Community collaboration **But the destination is worth it:** A world where geospatial expertise is no longer limited by technical barriers. Where hydrologists can analyze watersheds, ecologists can map habitats, and geologists can model terrain - all through natural conversation with AI that understands their domain. **That's the vision. That's what we're building.** --- ## Living Document This vision will evolve as we learn. Expected to be updated: - Quarterly: As we gain insights from real usage - Major releases: As capabilities mature - Community input: As users shape the direction **Current Phase**: Phase 2 ✅ Complete (v1.1.1) - Epistemic middleware operational - Vector/raster tool parity achieved - Cross-domain reflection validated **Next Phase**: Phase 3 - Workflow Intelligence (v2.0+) - Formal workflow composition - Multi-step orchestration - Pattern libraries --- ## References - [README.md](../README.md) - Current project status - [ROADMAP.md](ROADMAP.md) - Specific implementation plans - [ADR Directory](ADR/) - Technical decision records - [CONTRIBUTING.md](CONTRIBUTING.md) - How to help realize this vision --- *"The best way to predict the future is to invent it." - Alan Kay* *We're inventing a future where geospatial expertise is democratized.*