Allows loading Elasticsearch mappings (via OpenSearch format) as queryable data structures.
Allows loading Elasticsearch/OpenSearch index mappings as queryable data structures.
Enables loading data structures from Python Pydantic BaseModel classes for AI querying.
Enables loading data structure definitions from YAML files with full business context for AI querying.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@struct-mcp What does the cheese_id field represent?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Transform data structure definitions into queryable MCP servers. Define your data structures with business context and get an AI-queryable interface that can answer questions about field meanings, data lineage, and structure.
# Install
pip install struct-mcp
# Create a structure definition
echo "cheese_inventory:
description: 'Artisanal cheese catalog'
fields:
cheese_id:
type: string
description: 'Unique identifier for each cheese'
upstream_table: 'inventory.raw_cheese_data'
name:
type: string
description: 'Display name of the cheese'
stinkiness_level:
type: integer
nullable: true
description: 'Stinkiness rating from 1-10'
" > cheese.yaml
# Start MCP server
struct-mcp serve cheese.yamlRelated MCP server: Axon.MCP.Server
Load from multiple input formats:
YAML - Primary format with full business context
JSON Schema - Standard JSON Schema files
OpenSearch - Elasticsearch/OpenSearch mappings
Avro - Apache Avro schemas
Pydantic - Python BaseModel classes
Protocol Buffer - .proto message definitions
struct-mcp serve schema.yaml # YAML
struct-mcp serve schema.json # JSON Schema/OpenSearch/Avro
struct-mcp serve model.py # Pydantic
struct-mcp serve messages.proto # Protocol BufferOnce loaded, query your structures with natural language:
"What does the cheese_id field represent?"
"Which fields come from the inventory table?"
"What fields are nullable and why?"
"How is stinkiness_level calculated?"
"Show me all array fields"
from struct_mcp import StructMCP, MCPServer
# Load any format
smc = StructMCP.from_file("cheese.yaml")
# Query programmatically
fields = smc.get_fields("cheese_inventory")
nullable_fields = smc.get_fields("cheese_inventory", nullable=True)
# Convert between formats
opensearch_mapping = smc.to_opensearch()
pydantic_model = smc.to_pydantic()
# Start MCP server
server = MCPServer(smc)
server.start()See examples/ for sample files in all supported formats:
cheese_catalog.yaml - Artisanal cheese inventory
user_profiles.yaml - User data with preferences
financial_transactions.yaml - Payment processing metadata
For detailed setup, development, and API documentation, see setup.md.
MIT
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Hosted MCP server for AI-driven data ops. Create apps, manage schemas, and CRUD structured data.
Data-ontology maps of your business systems, served to AI agents over MCP.
The AI that understands raw data: Schema over your tables and databases, as MCP tools.
Governed data discovery, exact queries, decisions, simulations, and runtime utilities over MCP.
