toolform 0.0.1

Website · Documentation · Dashboard · PyPI · Paper (coming soon)

ToolForm decides how an agent shows its tools to a language model and reads back the calls. Give it your tools (OpenAI, Anthropic or MCP JSON, Python functions or TypeScript declarations) and your model: it recommends a schema format and an answer protocol from a controlled study of 129 formats, 31 answer protocols and 112 models, renders the tool block, parses and validates the reply, guards tool descriptions against hidden instructions, and handles long catalogues.

from toolform import Toolset, recommend, render, parse_reply

tools = Toolset.from_openai("tools.json")
rec = recommend(tools=tools, model="Qwen/Qwen3-8B")
print(rec.summary())   # xml_schema + xml_call, 93.14% expected (65.60 to 93.14 across the table values), checkpoint Qwen/Qwen3-8B

prompt = render(tools, rec.format_id, rec.protocol_id, request="Forecast for Denver, 5 days")
# send prompt.messages to any chat endpoint, then:
parsed = parse_reply(model_reply, tools, rec.protocol_id)
for call in parsed.calls:
    print(call.resolved_name, call.arguments, call.valid)

News

• 2026-10-20 · ToolForm 0.0.1 is on PyPI: pip install toolform. The advisor, 129 formats, 31 answer protocols, the guard, catalogue tools, a command line, an HTTP API and a web dashboard.
• 2026-10-20 · The website and the documentation are live.
• Coming soon · The paper, ToolForm: A Controlled Study of Tool Formatting for Agents, on arXiv.

What the study found

The way a model is shown its tools sets three things: the schema format it reads, the answer protocol it writes back in, and the grading rule that scores the call. Prior comparisons changed the first two together; ToolForm's study changed one at a time. Four rules came out:

And when you can reach the model, measure: testing eight formats on the target model (400 requests) reached 69.18%, against 64.35% for the best fitted rule and 57.97% for the JSON Schema default.

Install

pip install toolform                 # SDK and CLI
pip install "toolform[serve]"        # plus the HTTP API behind the dashboard

The advisor, the renderers, the parsers and the guard run offline from evidence that ships with the package. No GPU, no API key.

What's inside

Command line

toolform recommend --tools-file tools.json --model Qwen/Qwen3-8B   # format and protocol, with the evidence
toolform render tools.json --format xml_schema           # the tool block
toolform guard mcp_tools.json                            # scan a third-party manifest
toolform catalogue tools.json --context 32768            # plan a long catalogue
toolform serve                                           # the HTTP API

Documentation

Everything else, from the concepts to every SDK function, CLI option and API route, is in the documentation. Runnable examples are in examples/.

Contributing

Issues and pull requests are welcome; see CONTRIBUTING.md.

Citation

The paper, ToolForm: A Controlled Study of Tool Formatting for Agents, is coming soon. Until then, please cite the software:

@software{srivastava2026toolform,
  author  = {Srivastava, Gaurav},
  title   = {ToolForm: A Controlled Study of Tool Formatting for Agents},
  year    = {2026},
  url     = {https://github.com/ctrl-gaurav/toolform},
  version = {0.0.1}
}

License

Apache 2.0