Low-Code UI MCP Server
Analyzes runtime data objects to suggest optimal UI component instructions for a low-code renderer using structural heuristics. It automatically maps data patterns to specific UI elements like tables, carousels, and cards to streamline interface generation.
README
Low-Code UI MCP Server
This project provides a Python MCP server that inspects runtime data and returns UI instructions compatible with the low-code renderer in C:\Users\gregg\development\low-code\low-code\packages\ui.
The server is designed around the instruction types documented in C:\Users\gregg\development\low-code\low-code\README.md:
labellisttablecardaccordioncarousel
What It Does
The exposed MCP tool, suggest_ui_instructions, accepts a data object and returns:
instruction: a renderer-readyUIInstructionorUIInstruction[]analysis: the component decisions made for each fieldnotes: a short explanation of the heuristics used
Example selection behavior:
- arrays of primitive values become
list - small arrays of objects become
carousel - medium arrays of objects become
accordion - large, flat arrays of objects become
table - nested objects become
card
Install
cd C:\Users\gregg\development\mcp
python -m venv .venv
.venv\Scripts\Activate.ps1
python -m pip install -e .
Run
cd C:\Users\gregg\development\mcp
.venv\Scripts\Activate.ps1
low-code-ui-mcp
You can also run it directly:
python -m low_code_ui_mcp
Run with HTTP REST transport instead of stdio:
python -m low_code_ui_mcp --transport http --host 127.0.0.1 --port 8000
When running in HTTP mode:
- health check:
GET /health - non-blocking suggestion endpoint:
POST /api/v1/suggest-ui-instructions
Example request:
Invoke-RestMethod -Method Post -Uri "http://127.0.0.1:8000/api/v1/suggest-ui-instructions" -ContentType "application/json" -Body (@{
data = @{
title = "Quarterly Summary"
highlights = @("Revenue up 12%", "Churn down 3%")
products = @(
@{ name = "Alpha"; owner = "Team A"; score = 92 },
@{ name = "Beta"; owner = "Team B"; score = 88 }
)
}
} | ConvertTo-Json -Depth 20)
MCP Tool
suggest_ui_instructions
Parameters:
data: the object to analyzecarousel_max_items: maximum object-array size that still preferscarouseltable_min_items: minimum object-array size that starts preferringtabletable_max_columns: maximum distinct object keys that still counts as a flat tablemax_depth: recursion limit for nested instruction generation
Example request payload:
{
"data": {
"title": "Quarterly Summary",
"highlights": ["Revenue up 12%", "Churn down 3%"],
"products": [
{ "name": "Alpha", "owner": "Team A", "score": 92 },
{ "name": "Beta", "owner": "Team B", "score": 88 }
],
"accounts": [
{ "name": "Acme", "region": "NA", "status": "Active" },
{ "name": "Globex", "region": "EU", "status": "Active" },
{ "name": "Initech", "region": "APAC", "status": "Paused" },
{ "name": "Umbrella", "region": "NA", "status": "Active" },
{ "name": "Soylent", "region": "EU", "status": "Pilot" },
{ "name": "Wonka", "region": "NA", "status": "Active" },
{ "name": "Hooli", "region": "APAC", "status": "Active" },
{ "name": "Stark", "region": "NA", "status": "Active" }
]
}
}
Example response shape:
{
"instruction": [
{ "type": "label", "field": "title", "label": "Title" },
{ "type": "list", "field": "highlights", "label": "Highlights" },
{
"type": "carousel",
"field": "products",
"label": "Products",
"contents": [
{ "type": "label", "field": "name", "label": "Name" },
{ "type": "label", "field": "owner", "label": "Owner" },
{ "type": "label", "field": "score", "label": "Score" }
]
},
{ "type": "table", "field": "accounts", "label": "Accounts" }
]
}
Heuristics
- Primitive fields render as
label - Arrays of primitives render as
list - Empty arrays default to
list - Arrays of objects render as:
carouselwhen item count is smallaccordionwhen item count is medium or the objects are more nestedtablewhen the array is large and the objects are flat enough for columns
- Nested objects render as
cardwith child instructions inferred from the nested fields
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