FIELD_SPECS Authoring Guide

This guide explains how to write FIELD_SPECS for new configs so the Web UI forms and python -m tools.export_design_template stay in sync. It applies to any BaseConfig subclass (nodes, Memory, Thinking, Tooling, etc.).

1. Why FIELD_SPECS matter

UI forms rely on FIELD_SPECS to render inputs, defaults, and helper text.
The design template exporter reads FIELD_SPECS to populate yaml_template/design*.yaml and the mirrored files under frontend/public/.
Fields missing from FIELD_SPECS will not appear in the UI or generated templates.

2. Basic structure

FIELD_SPECS is a dict mapping field name → ConfigFieldSpec, usually declared inside the config class:

FIELD_SPECS = {
    "interpreter": ConfigFieldSpec(
        name="interpreter",
        display_name="Interpreter",
        type_hint="str",
        required=False,
        default="python3",
        description="Path to the Python executable",
    ),
    ...
}

Key attributes:

name: same as the YAML field.
display_name: optional human label shown in the UI; falls back to name when omitted.
type_hint: string describing the shape (str, list[str], dict[str, Any], etc.).
required: whether the UI treats the field as mandatory; usually False if a default exists.
default: scalar or JSON-serializable default value.
description: helper text/tooltips in forms and docs.
enum: array of allowed values (strings).
enumOptions: richer metadata per enum entry (label, description). Use it alongside enum for user-friendly dropdowns.
child: reference to another BaseConfig subclass for nested structures.

3. Authoring flow

Validate in from_dict – ensure YAML parsing enforces types and emits clear ConfigErrors (see entity/configs/python_runner.py).
Define FIELD_SPECS – cover all public fields with type, description, default, etc.
Handle dynamic fields – when options depend on registries or filesystem scans, override field_specs() and use replace() to inject real-time enum/description (e.g., FunctionToolEntryConfig.field_specs() lists functions on disk).
Export templates – after edits run:
```
python -m tools.export_design_template --output yaml_template/design.yaml --mirror frontend/public/design.yaml
```
The command uses the latest FIELD_SPECS to regenerate YAML templates and the frontend mirror—no manual edits needed.

4. Common patterns

Scalar fields: see entity/configs/python_runner.py for timeout_seconds (integer default + validation).
Nested lists: entity/configs/memory.py uses child=FileSourceConfig for file_sources, enabling repeatable subforms.
Dynamic enums: Node.field_specs() (around entity/configs/node.py:304) pulls node types from the registry and supplies enumOptions; FunctionToolEntryConfig.field_specs() builds enumerations from the function catalog.
Registry-driven descriptions: when calling register_node_type / register_memory_store / register_thinking_mode / register_tooling_type, always provide summary/description. Those strings populate enumOptions and keep dropdowns self-explanatory.
Optional blocks: combine required=False with sensible default values and make sure from_dict honors the same defaults.

5. Best practices

Keep descriptions user-friendly and clarify units (e.g., “Timeout (seconds)”).
Align defaults with parsing logic so UI expectations match backend behavior.
For nested configs, provide concise examples or cross-links so UI users understand the structure.
After changing FIELD_SPECS, re-run the export command and commit the updated templates/mirror files.

For more examples inspect entity/configs/model.py, entity/configs/tooling.py, or other existing node/Memory/Thinking configs.

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