RegisteredFunction

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://raw.githubusercontent.com/api-evangelist/amigo/main/json-schema/amigo-registered-function-schema.json",
  "title": "RegisteredFunction",
  "description": "The authored shape of a platform function.\n\nIdentical wire format whether it's authored as a YAML file in the\nrepo or POSTed to ``/v1/{ws}/functions/deploy``. The deploy\npipeline validates this, derives the JSON Schema for the LLM tool\nspec, and INSERTs a row into ``platform.functions`` with the next\nmonotonic ``version`` per ``(workspace_id, name)``. For\n``python`` / ``udtf`` rows, deploy ALSO issues\n``CREATE OR REPLACE FUNCTION`` against the warehouse to materialize\nthe UC UDF.",
  "x-generated": "2026-10-12",
  "x-method": "derived",
  "x-generator": "derive-json-schema.py",
  "x-source": "openapi/amigo-functions-api-openapi.yml#/components/schemas/RegisteredFunction",
  "properties": {
    "name": {
      "type": "string",
      "maxLength": 128,
      "minLength": 1,
      "pattern": "^[a-z][a-z0-9_]*$",
      "title": "Name"
    },
    "description": {
      "type": "string",
      "maxLength": 2048,
      "minLength": 1,
      "title": "Description"
    },
    "when_to_use": {
      "type": "string",
      "maxLength": 2048,
      "title": "When To Use",
      "default": ""
    },
    "function_type": {
      "type": "string",
      "enum": [
        "sql",
        "ai",
        "udtf",
        "python"
      ],
      "title": "Function Type",
      "default": "sql"
    },
    "returns": {
      "type": "string",
      "enum": [
        "table",
        "scalar"
      ],
      "title": "Returns",
      "default": "table"
    },
    "returns_type": {
      "type": "string",
      "enum": [
        "string",
        "integer",
        "number",
        "boolean"
      ],
      "title": "Returns Type",
      "default": "string"
    },
    "parameters": {
      "items": {
        "$ref": "#/$defs/platform_lib__platform_functions__models__Parameter"
      },
      "type": "array",
      "maxItems": 32,
      "title": "Parameters"
    },
    "returns_columns": {
      "items": {
        "$ref": "#/$defs/ReturnColumn"
      },
      "type": "array",
      "maxItems": 32,
      "title": "Returns Columns"
    },
    "body": {
      "type": "string",
      "maxLength": 8192,
      "minLength": 1,
      "title": "Body"
    },
    "timeout_ms": {
      "type": "integer",
      "maximum": 60000.0,
      "minimum": 100.0,
      "title": "Timeout Ms",
      "default": 30000
    }
  },
  "additionalProperties": false,
  "type": "object",
  "required": [
    "name",
    "description",
    "body"
  ],
  "$defs": {
    "FunctionExample": {
      "properties": {
        "input": {
          "additionalProperties": true,
          "type": "object",
          "title": "Input"
        },
        "output": {
          "title": "Output"
        },
        "description": {
          "type": "string",
          "title": "Description",
          "default": ""
        }
      },
      "additionalProperties": false,
      "type": "object",
      "required": [
        "input",
        "output"
      ],
      "title": "FunctionExample",
      "description": "One example call, surfaced to the LLM and the DC test panel.\n\nExamples are first-class metadata, not buried in a UC ``COMMENT``\nclause the way Databricks' native authoring requires. The DC\ntool-picker shows them; the LLM tool spec includes them inline."
    },
    "ReturnColumn": {
      "properties": {
        "name": {
          "type": "string",
          "maxLength": 64,
          "minLength": 1,
          "pattern": "^[a-z][a-z0-9_]*$",
          "title": "Name"
        },
        "type": {
          "type": "string",
          "enum": [
            "string",
            "integer",
            "number",
            "boolean"
          ],
          "title": "Type"
        },
        "description": {
          "type": "string",
          "maxLength": 512,
          "title": "Description",
          "default": ""
        }
      },
      "additionalProperties": false,
      "type": "object",
      "required": [
        "name",
        "type"
      ],
      "title": "ReturnColumn",
      "description": "One column in a UDTF's output schema.\n\nUC ``RETURNS TABLE(<col1> <type1>, <col2> <type2>, ...)`` requires\ndeclaring the schema at deploy time. This list anchors that\nschema for ``function_type=udtf`` rows. ``name`` becomes the\ncolumn name in the result rows; ``type`` maps to a Databricks\nSQL type via the same ``ParameterType`` Literal we use for inputs."
    },
    "platform_lib__platform_functions__models__Parameter": {
      "properties": {
        "name": {
          "type": "string",
          "maxLength": 64,
          "minLength": 1,
          "pattern": "^[a-z][a-z0-9_]*$",
          "title": "Name"
        },
        "type": {
          "type": "string",
          "enum": [
            "string",
            "integer",
            "number",
            "boolean"
          ],
          "title": "Type"
        },
        "description": {
          "type": "string",
          "maxLength": 512,
          "minLength": 1,
          "title": "Description"
        },
        "default": {
          "anyOf": [
            {
              "type": "string"
            },
            {
              "type": "integer"
            },
            {
              "type": "number"
            },
            {
              "type": "boolean"
            },
            {
              "type": "null"
            }
          ],
          "title": "Default"
        }
      },
      "additionalProperties": false,
      "type": "object",
      "required": [
        "name",
        "type",
        "description"
      ],
      "title": "Parameter",
      "description": "Typed declaration of one input parameter.\n\nFor SQL function types, ``name`` becomes the ``:name`` placeholder\nin the SQL template. For Python function types, ``name`` becomes\nthe positional argument name in ``def main(...)`` and the input\ncolumn name on the UC UDF signature.\n\nThe ``description`` propagates into the LLM tool spec (this is\nwhat Databricks' Agent Framework reads, and what Anthropic's\ntool-use spec exposes to the model). Missing descriptions\nsilently break tool selection — the registration layer rejects\nempty strings."
    }
  }
}