---
language:
- en
license: mit
pretty_name: Tool Calling Harness Training Corpus
tags:
- tool-calling
- agents
- codex
- opencode
- swival
- function-calling
task_categories:
- text-generation
---

# Tool Calling Harness Training Corpus

This dataset contains supervised tool-calling conversations and executable eval tasks for three local coding-agent harnesses:

- Swival
- opencode
- Codex CLI

The examples are OpenAI-style chat records. Assistant tool calls use a `tool_calls` array with JSON-encoded function arguments, followed by tool-result messages and final assistant responses. The corpus is intended for fine-tuning and evaluating local models that need to choose the right tool, use the correct argument shape, recover from harness-specific mistakes, and verify coding work through the actual harness.

## Files

Training splits:

- `swival/train.jsonl`: 6,064 examples
- `opencode/train.jsonl`: 6,290 examples
- `codex/train.jsonl`: 6,410 examples

Eval task specs:

- `swival/eval.jsonl`: 5 executable eval tasks
- `opencode/eval.jsonl`: 5 executable eval tasks
- `codex/eval.jsonl`: 5 executable eval tasks

Tool inventories:

- `swival/tool_inventory.json`
- `opencode/tool_inventory.json`
- `codex/tool_inventory.json`

Harness support files:

- `configs/opencode-qwen35.json`
- `configs/codex-qwen35.toml`
- `scripts/run_swival_eval.py`
- `scripts/run_opencode_eval.py`
- `scripts/run_codex_eval.py`
- `scripts/validate_dataset.py`
- `scripts/validate_opencode_dataset.py`
- `scripts/validate_codex_dataset.py`

## Local Harness Targets

The generated eval runners were tested against a local OpenAI-compatible `qwen35` server at `http://127.0.0.1:8000`.

Swival uses:

```sh
swival --profile omlx
```

opencode uses:

```sh
bunx opencode-ai@latest run --model qwen35/qwen35
```

Codex uses:

```sh
env OMLX_API_KEY=omlx codex -a never exec --json --ephemeral --skip-git-repo-check --sandbox workspace-write -c model_provider=\"omlx\" -m qwen35
```

## Validation

The source workspace validated the generated artifacts with:

```sh
uv run python scripts/validate_dataset.py data/swival_tool_calling_train.jsonl
uv run python scripts/validate_dataset.py data/swival_tool_calling_eval.jsonl
uv run python scripts/validate_opencode_dataset.py data/opencode_tool_calling_train.jsonl
uv run python scripts/validate_opencode_dataset.py data/opencode_tool_calling_eval.jsonl
uv run python scripts/validate_codex_dataset.py data/codex_tool_calling_train.jsonl
uv run python scripts/validate_codex_dataset.py data/codex_tool_calling_eval.jsonl
uv run python scripts/run_swival_eval.py --self-check
uv run python scripts/run_opencode_eval.py --self-check
uv run python scripts/run_codex_eval.py --self-check
```

Live smoke tests were also run for opencode and Codex against the local `qwen35` endpoint. Swival, opencode, and Codex each have their own tool naming and argument conventions, so the examples intentionally avoid collapsing them into one generic schema.

## Schema

Training rows use:

```json
{
  "id": "string",
  "split": "train",
  "tags": ["harness", "tool_name"],
  "messages": [
    {"role": "user", "content": "task"},
    {
      "role": "assistant",
      "content": "",
      "tool_calls": [
        {
          "id": "call_1",
          "type": "function",
          "function": {
            "name": "tool_name",
            "arguments": "{\"key\":\"value\"}"
          }
        }
      ]
    },
    {"role": "tool", "tool_call_id": "call_1", "name": "tool_name", "content": "tool result"},
    {"role": "assistant", "content": "final response"}
  ]
}
```

Eval rows define disposable workspace files plus verifiers such as exact file contents, substring checks, missing-file checks, JSON field checks, and command checks.