tooluniverse-custom-tool

Adding Custom Tools to ToolUniverse

When to create a custom tool: Create one if you need to access an API that ToolUniverse doesn't cover, or if you need a specialized data transformation that no existing tool provides. Start with the JSON config approach (simplest — no Python needed); escalate to a Python class only if you need custom response parsing or stateful logic.

Three ways to add tools — pick the one that fits your needs:

Approach	When to use
JSON config	REST API with standard request/response — no coding needed
Python class (workspace)	Custom logic for local/private use only
Plugin package	Reusable tools you want to share or install via pip

---

Option A — Workspace tools (local use)

Tools in .tooluniverse/tools/ are auto-discovered at startup. No installation needed.

mkdir -p .tooluniverse/tools

JSON config

Create .tooluniverse/tools/my_tools.json:

[
  {
    "name": "MyAPI_search",
    "description": "Search my internal database. Returns matching records with id, title, and score.",
    "type": "BaseRESTTool",
    "fields": {
      "endpoint": "https://my-api.example.com/search"
    },
    "parameter": {
      "type": "object",
      "properties": {
        "q": {
          "type": "string",
          "description": "Search query"
        },
        "limit": {
          "type": ["integer", "null"],
          "description": "Max results to return (default 10)"
        }
      },
      "required": ["q"]
    }
  }
]

One JSON file can define multiple tools — just add more objects to the array.

For the full JSON field reference, see references/json-tool.md.

Python class

Create .tooluniverse/tools/my_tool.py:

from tooluniverse.tool_registry import register_tool

@register_tool
class MyAPI_search:
    name = "MyAPI_search"
    description = "Search my internal database. Returns matching records with id, title, and score."
    input_schema = {
        "type": "object",
        "properties": {
            "q": {"type": "string", "description": "Search query"},
            "limit": {"type": "integer", "description": "Max results (default 10)"}
        },
        "required": ["q"]
    }

    def run(self, q: str, limit: int = 10) -> dict:
        import requests
        resp = requests.get(
            "https://my-api.example.com/search",
            params={"q": q, "limit": limit},
            timeout=30,
        )
        resp.raise_for_status()
        return {"status": "success", "data": resp.json()}

Note: workspace Python tools use run(self, **named_params) — arguments are unpacked as keyword arguments matching the input_schema properties.

For the full Python class reference, see references/python-tool.md.

Test workspace tools

# Uses test_examples from the tool's JSON config — zero config needed
tu test MyAPI_search

# Single ad-hoc call
tu test MyAPI_search '{"q": "test"}'

# Full config with assertions
tu test --config my_tool_tests.json

tu test automatically runs these checks on every call:

• Result is not None or empty
• return_schema validation — validates result["data"] against the JSON Schema defined in return_schema (if present)
• expect_status and expect_keys — only if set in the config file

Gotchas: (1) tu test does NOT verify non-empty results — [] passes schema validation. Use test_examples args that return real data. (2) Verify test_examples manually first with urllib (not curl) to confirm the API returns JSON, not HTML. Use 2-4 broad keywords.

Add test_examples and return_schema to JSON config for best coverage. tu test validates result["data"] against return_schema (match "type": "array" or "type": "object" to your data shape).

Optional my_tool_tests.json for extra assertions (expect_status, expect_keys).

Use with MCP server

Tools in .tooluniverse/tools/ are auto-available via tu serve. Workspace priority: --workspace flag → TOOLUNIVERSE_HOME env → ./.tooluniverse/ → ~/.tooluniverse/.

To use a different tools directory, add sources: [./my-custom-tools/] in .tooluniverse/profile.yaml and start with tooluniverse --load .tooluniverse/profile.yaml.

---

Option B — Plugin package (shareable, pip-installable)

Use this when you want to distribute tools as a reusable Python package that other users can install with pip install. The plugin package has the same directory layout as a workspace, plus a pyproject.toml that declares the entry point.

Package layout

my_project_root/           # directory containing pyproject.toml
    pyproject.toml
    my_tools_package/      # importable Python package (matches entry-point value)
        __init__.py        # minimal — one-line docstring, no registration code
        my_api_tool.py     # tool class(es) with @register_tool
        data/
            my_api_tools.json  # JSON tool configs (type must match registered class name)
        profile.yaml       # optional: name, description, required_env

JSON config files are discovered from both data/ and the package root directory. The convention is data/.

pyproject.toml entry point

[project.entry-points."tooluniverse.plugins"]
my-tools = "my_tools_package"

The value (my_tools_package) must be the importable Python package name.

Python class in a plugin package

Plugin package tools use BaseTool and receive all arguments as a single Dict:

import requests
from typing import Dict, Any
from tooluniverse.base_tool import BaseTool
from tooluniverse.tool_registry import register_tool

@register_tool("MyAPITool")
class MyAPITool(BaseTool):
    """Tool description here."""

    def __init__(self, tool_config: Dict[str, Any]):
        super().__init__(tool_config)
        self.timeout = tool_config.get("timeout", 30)
        fields = tool_config.get("fields", {})
        self.operation = fields.get("operation", "search")

    def run(self, arguments: Dict[str, Any]) -> Dict[str, Any]:
        query = arguments.get("query", "")
        if not query:
            return {"error": "query parameter is required"}
        try:
            resp = requests.get(
                "https://my-api.example.com/search",
                params={"q": query},
                timeout=self.timeout,
            )
            resp.raise_for_status()
            return {"status": "success", "data": resp.json()}
        except requests.exceptions.RequestException as e:
            return {"error": str(e)}

Key differences from the workspace pattern:

• Inherit from BaseTool (from tooluniverse.base_tool)
• @register_tool("ClassName") takes the class name as a string argument
• run(self, arguments: Dict) receives all arguments in a single dict — extract them with .get()
• __init__ receives tool_config dict; call super().__init__(tool_config) first

JSON config in a plugin package

Place configs in data/my_api_tools.json. The "type" field must match the string passed to @register_tool(...):

[
  {
    "name": "MyAPI_search",
    "description": "Search my API. Returns matching records.",
    "type": "MyAPITool",
    "fields": { "operation": "search" },
    "parameter": {
      "type": "object",
      "properties": {
        "query": { "type": "string", "description": "Search query" },
        "limit": { "type": ["integer", "null"], "description": "Max results" }
      },
      "required": ["query"]
    }
  }
]

__init__.py

Keep minimal — just a docstring. The plugin system auto-imports all .py files via _discover_entry_point_plugins(), so @register_tool decorators fire automatically. Optional: add from . import my_api_tool for IDE support (idempotent). Do NOT add registration logic or JSON loading here.

Install and verify

pip install -e /path/to/my_project_root
cd /path/to/my_project_root   # MUST run from plugin repo directory
tu test MyAPI_search '{"query": "test"}'

Must pip install -e first. Run tu test from plugin repo dir (workspace auto-detection needs .tooluniverse/). Add test_examples to JSON config for zero-config testing. Use tu info MyAPI_search to confirm the tool loaded.

---

Offline / pure-computation tools

Calculator tools (no HTTP) follow the plugin-package pattern but skip the HTTP layer. Key design patterns:

• Preset lookup tables: Define Dict[str, float] at module level. Resolution priority: explicit value → preset name → default. Include presets in metadata for discoverability.
• Bidirectional equations: Expose as separate operation values in a single tool. Use "fields": {"operation": "default_op"} in JSON config.
• Physical constants: Define at module level (_MU0 = 4*pi*1e-7, etc.). Material-specific values as named dicts.
• Multi-output: Return all related results in data (e.g., temperature + headroom + pass/fail) rather than forcing multiple calls.

For complete patterns, see references/python-tool.md.