zzstoatzz.io
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toolcheck: model-agnostic tool-calling capability harness
pydantic-ai over any OpenAI-compatible endpoint (ollama/vLLM/LM Studio).
scores behaviour — tool selection, arg routing, abstention, parallel,
numeric fidelity, dependent chains — not prose. ships a gemma3 Modelfile
that exposes tools through ollama's otherwise tool-less stock template.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
author
zzstoatzz
co-author
Claude Opus 4.8 (1M context)
date
(Jun 3, 2026, 2:55 PM -0500)
commit
03b27963
03b27963216d39d99b562c08ab694440ff43974e
+4297
+69
README.md
+69
README.md
···
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# toolcheck
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Does this local model *actually* do tool calling? Point `toolcheck` at any
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OpenAI-compatible endpoint (ollama, vLLM, LM Studio, llama.cpp server) and it
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runs a small behavioural suite through [pydantic-ai](https://ai.pydantic.dev/) —
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real tool dispatch, no regex parsing — then scores what the model did.
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It checks the things that actually break when you swap a frontier model for a
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small local one:
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| case | probes |
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|---|---|
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| `single` | basic single-tool selection |
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| `structured-args` | multi-arg call with correct argument routing |
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| `abstain` | does **not** invent a tool call for chit-chat |
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| `parallel` | two independent calls in one turn |
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| `numeric-fidelity` | passes numbers as ints, not strings |
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| `dependent-chain` | two-step: resolve A, feed it into B |
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## quickstart
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```bash
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# any OpenAI-compatible endpoint works; ollama is the default
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uvx toolcheck llama3.1:8b
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# explicit endpoint + verbose (shows every call's args and the reply)
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uvx toolcheck my-model -b http://localhost:8000/v1 -k sk-anything -v
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```
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Or from a clone:
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```bash
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uv run toolcheck llama3.1:8b -v
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```
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Exit code is `0` only if every case passes — drop it in CI to gate a model swap.
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## the catch with small models: the *serving layer*, not the model
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Most small open models can reason about tools fine. What breaks is the serving
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wrapper: it either doesn't expose tools at all, or doesn't parse the model's
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output back into structured `tool_calls`.
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ollama is the canonical example. Its stock `gemma3` template references `.Tools`
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zero times, so the API **400s every tool request** before the model sees it.
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`modelfiles/gemma3-tools.Modelfile` fixes that — it renders the tool list into
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the opening turn and constrains the model to emit a bare JSON array (no markdown
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fences) that ollama's parser recognizes:
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```bash
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ollama create gemma3-tools -f modelfiles/gemma3-tools.Modelfile
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toolcheck gemma3-tools -v # 6/6
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```
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The same idea generalizes: if a model fails every case with a 400 or leaks raw
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JSON into the reply text, the fix is the template/parser, not the model.
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On vLLM, use the built-in per-model tool parser
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(`--enable-auto-tool-choice --tool-call-parser ...`) instead of a Modelfile.
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## extending the suite
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`src/toolcheck/suite.py` is data. Add a tool in `register_tools`, append a
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`Case` with a validator over the recorded call sequence, done. Validators get
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the full `list[Call]` (name + args) so you can assert on arguments, ordering,
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and abstention — not just prose.
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## license
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MIT.
+28
modelfiles/gemma3-tools.Modelfile
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modelfiles/gemma3-tools.Modelfile
···
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FROM gemma3:12b
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TEMPLATE """{{- range $i, $_ := .Messages }}
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{{- $last := eq (len (slice $.Messages $i)) 1 }}
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{{- if or (eq .Role "user") (eq .Role "system") }}<start_of_turn>user
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{{ if and $.Tools (eq $i 0) }}You have access to the following functions. To call one or more, respond with ONLY a raw JSON array of calls, e.g. [{"name": "fn", "arguments": {"a": 1}}]. Do NOT wrap it in markdown code fences. Do NOT add any prose. Ensure the JSON is valid. If no function is needed, just answer normally.
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{{ range $.Tools }}{{ . }}
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{{ end }}
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{{ end }}{{ .Content }}<end_of_turn>
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{{ if $last }}<start_of_turn>model
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{{ end }}
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{{- else if eq .Role "assistant" }}<start_of_turn>model
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{{ .Content }}{{ if .ToolCalls }}[{{ range $j, $tc := .ToolCalls }}{{ if $j }}, {{ end }}{"name": "{{ $tc.Function.Name }}", "arguments": {{ $tc.Function.Arguments }}}{{ end }}]{{ end }}{{ if not $last }}<end_of_turn>
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{{ end }}
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{{- else if eq .Role "tool" }}<start_of_turn>user
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```tool_response
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{{ .Content }}
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```<end_of_turn>
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{{ if $last }}<start_of_turn>model
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{{ end }}
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{{- end }}
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{{- end }}"""
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PARAMETER stop <end_of_turn>
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PARAMETER temperature 0.1
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PARAMETER top_k 64
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PARAMETER top_p 0.95
+22
pyproject.toml
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pyproject.toml
···
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[project]
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name = "toolcheck"
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version = "0.1.0"
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description = "Check whether a local LLM can actually do tool calling — via pydantic-ai against any OpenAI-compatible endpoint."
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readme = "README.md"
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requires-python = ">=3.10"
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license = { text = "MIT" }
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dependencies = [
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"pydantic-ai>=1.0",
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"typer>=0.12",
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"rich>=13",
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]
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[project.scripts]
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toolcheck = "toolcheck.cli:app"
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[build-system]
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requires = ["hatchling"]
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build-backend = "hatchling.build"
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[tool.hatch.build.targets.wheel]
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packages = ["src/toolcheck"]
+15
src/toolcheck/__init__.py
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src/toolcheck/__init__.py
···
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"""toolcheck — does this local model actually do tool calling?"""
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from .runner import Result, build_agent, run_suite
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from .suite import SUITE, Case, Recorder, expect_names, expect_none
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__all__ = [
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"SUITE",
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"Case",
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"Recorder",
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"Result",
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"build_agent",
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"expect_names",
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"expect_none",
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"run_suite",
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]
+53
src/toolcheck/cli.py
+53
src/toolcheck/cli.py
···
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"""toolcheck CLI — point it at a model and see if it can really call tools."""
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from __future__ import annotations
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import asyncio
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import typer
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from rich.console import Console
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from rich.table import Table
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from .runner import run_suite
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app = typer.Typer(add_completion=False, help=__doc__)
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console = Console()
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@app.command()
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def main(
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model: str = typer.Argument(..., help="model name on the endpoint, e.g. 'gemma3-tools' or 'llama3.1:8b'"),
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base_url: str = typer.Option("http://localhost:11434/v1", "--base-url", "-b", help="OpenAI-compatible endpoint (ollama default shown)"),
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api_key: str = typer.Option("ollama", "--api-key", "-k", help="api key; any non-empty string for local servers"),
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verbose: bool = typer.Option(False, "--verbose", "-v", help="show each call's args and the model's reply"),
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):
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"""Run the tool-calling suite and print a scorecard."""
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results = asyncio.run(run_suite(model, base_url, api_key))
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table = Table(title=f"toolcheck · {model} @ {base_url}", header_style="bold")
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table.add_column("case")
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table.add_column("result")
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table.add_column("detail" if verbose else "what it probes")
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for r in results:
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status = "[green]PASS[/]" if r.passed else ("[yellow]ERROR[/]" if r.error else "[red]FAIL[/]")
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info = r.error or (r.detail if (verbose or not r.passed) else r.case.note)
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table.add_row(r.case.id, status, info)
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console.print(table)
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if verbose:
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for r in results:
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console.print(f"\n[bold]{r.case.id}[/] — {r.case.prompt}")
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for c in r.calls:
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console.print(f" → {c.name}({c.args})")
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if r.reply:
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console.print(f" reply: {r.reply[:200]}")
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passed = sum(r.passed for r in results)
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total = len(results)
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color = "green" if passed == total else "yellow" if passed else "red"
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console.print(f"\n[bold {color}]{passed}/{total} passed[/]")
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raise typer.Exit(0 if passed == total else 1)
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if __name__ == "__main__":
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app()
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src/toolcheck/runner.py
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src/toolcheck/runner.py
···
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"""run the suite against an OpenAI-compatible endpoint via pydantic-ai."""
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from __future__ import annotations
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from dataclasses import dataclass
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from pydantic_ai import Agent
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from pydantic_ai.models.openai import OpenAIChatModel
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from pydantic_ai.providers.openai import OpenAIProvider
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from .suite import SUITE, Case, Recorder, register_tools
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SYSTEM_PROMPT = "You are a precise assistant. Use the provided tools when they are needed to answer accurately."
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@dataclass
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class Result:
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case: Case
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passed: bool
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detail: str
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calls: list
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reply: str
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error: str | None = None
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def build_agent(model_name: str, base_url: str, api_key: str) -> Agent[Recorder]:
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model = OpenAIChatModel(
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model_name,
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provider=OpenAIProvider(base_url=base_url, api_key=api_key),
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)
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agent = Agent(model, deps_type=Recorder, system_prompt=SYSTEM_PROMPT)
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register_tools(agent)
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return agent
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async def run_case(agent: Agent[Recorder], case: Case) -> Result:
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rec = Recorder()
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try:
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res = await agent.run(case.prompt, deps=rec)
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except Exception as e: # endpoint/parse/loop failures are themselves data
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return Result(case, False, "", rec.calls, "", error=f"{type(e).__name__}: {e}")
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passed, detail = case.validate(rec.calls)
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reply = (res.output or "").strip()
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return Result(case, passed, detail, rec.calls, reply)
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async def run_suite(
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model_name: str, base_url: str, api_key: str, cases: list[Case] | None = None
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) -> list[Result]:
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agent = build_agent(model_name, base_url, api_key)
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results = []
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for case in cases or SUITE:
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results.append(await run_case(agent, case))
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return results
+157
src/toolcheck/suite.py
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src/toolcheck/suite.py
···
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"""the capability suite: tools the model can call, and cases that probe it.
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a "case" is a prompt plus a validator over the *sequence of tool calls* the
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model actually made. we score behaviour, not prose — did the right tools fire,
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with the right arguments, and did the model correctly abstain when it should?
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to extend: add a tool to `register_tools` and append a `Case`. nothing else
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needs to change.
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Callable
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from pydantic_ai import Agent, RunContext
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@dataclass
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class Call:
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name: str
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args: dict
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@dataclass
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class Recorder:
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"""deps object threaded through a run; tools append their calls here."""
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calls: list[Call] = field(default_factory=list)
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# --- tools ----------------------------------------------------------------
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# deliberately small but varied: a 1-arg lookup, a multi-arg structured query,
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# a numeric op (type fidelity), and a second lookup that enables a dependent
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# two-step chain (capital -> weather).
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def register_tools(agent: Agent[Recorder]) -> None:
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@agent.tool
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async def get_weather(ctx: RunContext[Recorder], city: str) -> str:
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"""Get the current weather for a city."""
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ctx.deps.calls.append(Call("get_weather", {"city": city}))
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return f"{city}: 18C, clear"
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@agent.tool
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async def get_capital(ctx: RunContext[Recorder], country: str) -> str:
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"""Get the capital city of a country."""
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ctx.deps.calls.append(Call("get_capital", {"country": country}))
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caps = {"france": "Paris", "japan": "Tokyo", "germany": "Berlin"}
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return caps.get(country.strip().lower(), "Unknown")
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@agent.tool
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async def search_records(
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ctx: RunContext[Recorder], query: str, collection: str = ""
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) -> str:
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"""Search records by a query string and optional collection name."""
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ctx.deps.calls.append(
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Call("search_records", {"query": query, "collection": collection})
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)
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return f"3 records matching {query!r} in {collection or 'any'}"
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+
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@agent.tool
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async def add(ctx: RunContext[Recorder], a: int, b: int) -> int:
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"""Add two integers."""
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ctx.deps.calls.append(Call("add", {"a": a, "b": b}))
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return a + b
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# --- validators -----------------------------------------------------------
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Validator = Callable[[list[Call]], "tuple[bool, str]"]
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def names(calls: list[Call]) -> list[str]:
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return [c.name for c in calls]
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+
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+
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def expect_names(*expected: str, ordered: bool = False) -> Validator:
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"""built-in validator: the multiset (or ordered list) of called tool names."""
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def check(calls: list[Call]) -> tuple[bool, str]:
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got = names(calls)
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want = list(expected)
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ok = got == want if ordered else sorted(got) == sorted(want)
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rel = "in order" if ordered else "as a set"
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return ok, f"want {want} {rel}, got {got}"
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return check
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+
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+
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def expect_none() -> Validator:
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"""the model should answer directly and call nothing."""
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def check(calls: list[Call]) -> tuple[bool, str]:
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return (not calls), f"want no calls, got {names(calls)}"
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return check
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+
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+
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@dataclass
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class Case:
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id: str
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prompt: str
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validate: Validator
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note: str = ""
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# --- the suite ------------------------------------------------------------
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SUITE: list[Case] = [
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Case(
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"single",
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"What's the weather in Paris?",
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expect_names("get_weather"),
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"basic single-tool selection",
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),
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Case(
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"structured-args",
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"Search for posts about pydantic in the app.bsky.feed.post collection.",
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lambda calls: (
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names(calls) == ["search_records"]
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and calls[0].args.get("query", "").lower().find("pydantic") >= 0
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and "app.bsky.feed.post" in calls[0].args.get("collection", ""),
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f"want search_records(query~pydantic, collection=app.bsky.feed.post), got {[(c.name, c.args) for c in calls]}",
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),
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"multi-arg structured call with correct argument routing",
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),
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Case(
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"abstain",
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"Hi! How are you doing today?",
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expect_none(),
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"must NOT invent a tool call for chit-chat",
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),
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Case(
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"parallel",
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"What's the weather in Tokyo and also in Berlin?",
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expect_names("get_weather", "get_weather"),
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"two independent calls in one turn",
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),
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Case(
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"numeric-fidelity",
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"What is 47 plus 58?",
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lambda calls: (
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names(calls) == ["add"]
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and calls[0].args.get("a") in (47, 58)
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and calls[0].args.get("b") in (47, 58),
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f"want add(47, 58) with integer args, got {[(c.name, c.args) for c in calls]}",
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),
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"passes numbers as ints, not strings",
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),
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Case(
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"dependent-chain",
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"What's the weather in the capital of France? Use the tools.",
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expect_names("get_capital", "get_weather", ordered=True),
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"two-step: resolve capital, then feed it to weather",
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),
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+
]
+3893
uv.lock
+3893
uv.lock
···
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version = 1
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+
revision = 3
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+
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+
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