0.2.0

adaptive-card-tools

Build modern, deterministic Adaptive Cards from small, forgiving agent tool calls. The model describes what to show; Python repairs the request, applies a consistent visual design, and returns a complete card.

Designed for small models such as GPT-4.1 mini and Gemini 2.5 Flash:

• Short prompt. The tool description is ~1,200 tokens of examples, not a schema manual (it was ~8,500 in 0.1.x).
• Repair, don't reject. Wrong type names, Adaptive Card JSON, style keys, aliases, typos, strings for numbers and dates, odd option shapes, wrapped or broken JSON: all are interpreted. Errors are reserved for requests that are unreadable, empty, or over a safety limit.
• Accurate. Repairs change the form of a value, never its facts. Text is never rewritten or truncated, numbers are parsed but never estimated, missing chart points are not filled with zeros, and anything that can't be read exactly is left out with a warning. Every repair is listed in metadata.
• Modern and stable. The model never chooses styling. Every card gets the same hierarchy (title, status badge, KPI tiles, data bars, shaded table headers, primary/destructive buttons) built from primitives that render on every Adaptive Cards host.

Python 3.10+, no runtime dependencies. Import name: adaptive_card_tools.

Rendered with Microsoft's adaptivecards JS SDK. 0.2 with host_config() and examples/card-theme.css (the middle card is the deliberately messy call from examples.py):

0.1 output for comparison

Install and use

python -m pip install .

from adaptive_card_tools import build_adaptive_card

result = build_adaptive_card({
    "title": "Weekly sales",
    "subtitle": "Week ending 11 Sep 2026",
    "status": "On track",
    "elements": [
        {"type": "metrics", "items": [
            {"label": "Revenue", "value": "$48,200", "change": "+12%"},
            {"label": "Orders", "value": "312", "change": "-3%"}]},
        {"type": "chart", "chart_type": "bar", "title": "Revenue by region",
         "data": {"North": 18200, "South": 12100, "West": 17900}},
    ],
})
result["status"]          # "successful"
result["response"]        # the Adaptive Card JSON for your frontend
result["message"]         # a short instruction for the model

Add to an agent

The function is a plain Python tool; its docstring is the model's prompt (see docs/TOOL_DOCSTRING.md).

# Google ADK
from google.adk.agents import Agent
from adaptive_card_tools import build_adaptive_card

agent = Agent(name="card_assistant", model="gemini-2.5-flash", tools=[build_adaptive_card],
              instruction="When the answer is a form, summary, comparison, dashboard, approval, "
                          "table or chart, call build_adaptive_card once with the complete content.")

# LangChain, with the rich schema (element types as an enum, which small models follow well)
from langchain.agents import create_agent
from langchain_core.tools import StructuredTool
from adaptive_card_tools import build_adaptive_card, tool_schema

schema = tool_schema()
card_tool = StructuredTool.from_function(build_adaptive_card, name=schema["name"],
                                         description=schema["description"], args_schema=schema["parameters"])
agent = create_agent(model="openai:gpt-4.1-mini", tools=[card_tool])

If a provider rejects a free-form object parameter (some Gemini setups and strict tool modes do), use build_adaptive_card_json(request_json: str). Its parser also repairs single quotes, trailing commas, comments, Python True/None, code fences, prose around the JSON, and unclosed brackets.

make_adaptive_card_tool(BuilderConfig(...), json_input=False) returns a tool bound to your settings. describe_adaptive_card(type) is an optional helper tool; small models rarely need it.

What the model writes

{"title": "Book a demo", "elements": [
  {"type": "text", "text": "Pick a time that suits you."},
  {"type": "text_input", "id": "name", "label": "Full name", "required": true},
  {"type": "date_input", "id": "day", "label": "Preferred day"},
  {"type": "choice", "id": "size", "label": "Team size", "options": ["1-10", "11-50", "51+"]}],
 "submit_label": "Request demo"}

Card-level fields: title, subtitle, status (a coloured badge next to the title), elements, submit_label, buttons. Forms get a Submit button automatically; "buttons": [] suppresses it. Every field name and alias is listed in docs/ELEMENT_REFERENCE.md.

Normalization

What small models actually send, and what happens:

Nothing is guessed that would change meaning: an unmatched default, an unreadable date ("next Tuesday", "March 5" with no year), an out-of-range value, or a bad URL is left out and reported in metadata.warnings, which is also summarized in message so the model can decide whether to retry.

Visual design

The design lives in src/adaptive_card_tools/theme.py. Meaning is carried by typography, text colour and separators, which every host renders:

• Header: large title, subtle subtitle, status badge aligned right.
• metrics: big-number tiles with ▲/▼ changes in green/red (lower_is_better flips colours).
• Charts in the portable profile: label | bar | value rows drawn with block glyphs, with exact values and pie percentages as text. Progress bars have a track. Ratings show stars plus the exact value.
• Tables: shaded bold header row, numeric columns right-aligned, content-based column widths.
• Sections: bold title, a separator at the top level. Columns: tiles.
• Buttons: the primary action is positive; reject/delete/decline are destructive.

Container backgrounds (tiles, table header shading) depend on the host config. They show in Teams and with the recommended config, and fall back to plain columns elsewhere, so nothing is lost. For the JS SDK, pass adaptive_card_tools.host_config() to new AdaptiveCards.HostConfig(...) and include examples/card-theme.css for buttons and inputs; see examples/frontend.js.

Renderer profiles

portable (default) emits core Adaptive Cards 1.5 only. microsoft emits native extensions (charts, Badge, Rating, ProgressBar, ProgressRing, Input.Rating) with a core fallback on each.

from adaptive_card_tools import BuilderConfig, make_adaptive_card_tool

card_tool = make_adaptive_card_tool(BuilderConfig(
    profile="microsoft",
    native_types=("Badge", "Rating", "Chart.Line"),  # optional allow-list
    available_width_px=1200,
    day_first=False,
))

Native charts are only used when the data can be drawn exactly; series with gaps or non-numeric values use the table fallback. The model cannot select the profile, styling or schema version.

Result envelope

{
  "status": "successful",          # or "error"
  "suggestedWidth": "60%",         # 50%, 60%, 80%, 100% or "100%+" (a signal, not CSS)
  "response": {...},               # the card; forward unchanged to the renderer
  "message": "Card built and ready to display. ...",  # for the model
  "metadata": {
    "profile": "portable", "schemaVersion": "1.5",
    "inputIds": ["name", "day", "size"],
    "choiceValues": {"size": [{"title": "1-10", "value": "1-10"}, ...]},
    "layout": {"widthPercent": 60, "estimatedMinWidthPx": 480, "overflow": "none", ...},
    "warnings": [],                # content left out or reinterpreted
    "normalizations": [...]        # every harmless repair
  }
}

On error, response and message explain the problem and errors holds {path, code, message}. Errors happen only for unreadable JSON, a request with nothing to display, or configured limits (bytes, depth, nodes, elements, output size).

Width is an estimate from structural minima (columns add, stacked content takes the widest); the frontend adapter in examples/frontend.js applies it and scrolls when needed.

Application responsibilities

• Render response unchanged; handle Action.Submit and Action.OpenUrl in the frontend.
• Validate and authorize every submission on the server. Generated ids are deterministic for the same request, not stable across edits; give explicit ids when your backend expects particular keys.
• URLs are validated (http/https, no credentials) but never fetched or allow-listed.
• No network access, randomness, clock or LLM call happens in the build path.

Examples

src/adaptive_card_tools/examples.py has a product comparison, expense form, weekly review, approval, questionnaire, KPI dashboard, and a deliberately messy small-model call. python examples/export_examples.py regenerates examples/generated (spec, tool call, and both profile outputs) and the docs.

Test and build

PYTHONPATH=src python -m unittest discover -s tests -v       # core suite, stdlib only
python -m pip install -e '.[test,integration]'                # + official schema, LangChain, ADK
python -m unittest discover -s tests -v
python -m pip install -e '.[dev]' && python -m build && python -m twine check dist/*

See docs/VALIDATION.md for what has been verified.

Sources

• Adaptive Cards documentation and schema explorer
• Official core schema
• Microsoft host features, ratings and responsive layouts
• Google ADK function tools
• LangChain tools