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Model: intuit/agent-tool-optimizer
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+language:
+- en
+datasets:
+- intuit/tool-optimizer-dataset
+base_model:
+- Qwen/Qwen3-4B-Instruct-2507
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- agents
+- tool-use
+- sft
+- documentation
+- text-generation
+---
+
+# Agent Tool Optimizer (`intuit/agent-tool-optimizer`)
+
+`intuit/agent-tool-optimizer` is a **supervised fine-tuned (SFT)** model that rewrites **tool / API descriptions** to be more usable by **LLM agents**. Given a tool name, a parameter schema, and a baseline (often human-written) description, the model produces an improved description that helps an agent:
+
+- decide **when to use vs. not use** the tool
+- generate **valid parameters** (required vs optional, constraints, defaults)
+- avoid common mistakes and likely validation failures
+
+This model is trained to work in a **trace-free** setting at inference time (i.e., **no tool execution traces required**).
+
+For the accompanying codebase (inference + training), see: [Agent Tool Interface Optimizer](https://github.com/intuit-ai-research/tool-optimizer).
+
+---
+
+## What problem does this solve?
+
+Tool interfaces (descriptions + parameter schemas) are the “contract” between agents and tools, but are typically written for humans. When descriptions under-specify **required parameters**, omit **constraints**, or fail to explain **tool boundaries**, agent performance can plateau and can degrade as the number of available tools increases.
+
+We study tool interface improvement as a scalable complement to agent fine-tuning, and propose **Trace-Free+**: a curriculum-learning approach that transfers knowledge learned from trace-rich training to trace-free inference for unseen tools.
+
+---
+
+## Paper (arXiv)
+
+This model is released alongside the preprint:
+
+- **Learning to Rewrite Tool Descriptions for Reliable LLM-Agent Tool Use**  
+  Ruocheng Guo, Kaiwen Dong, Xiang Gao, Kamalika Das  
+  arXiv:2602.20426 (2026) — [paper](https://arxiv.org/abs/2602.20426)
+
+### Citation
+
+```bibtex
+@misc{guo2026learningrewritetooldescriptions,
+      title={Learning to Rewrite Tool Descriptions for Reliable LLM-Agent Tool Use}, 
+      author={Ruocheng Guo and Kaiwen Dong and Xiang Gao and Kamalika Das},
+      year={2026},
+      eprint={2602.20426},
+      archivePrefix={arXiv},
+      primaryClass={cs.AI},
+      url={https://arxiv.org/abs/2602.20426}, 
+}
+```
+
+---
+
+## Recommended prompt (trace-free)
+
+This is the **canonical inference prompt** used for trace-free tool description generation (also available as `tool_prompt.txt` in the `tool-optimizer` repo).
+
+```
+You are an API documentation specialist.
+
+Rewrite the API description so an AI agent can:
+1) Decide when to use this API
+2) Generate valid parameters
+
+Inputs:
+- API name: {tool_name}
+- Parameter schema: {parameter_json}
+- Baseline description: {original_description}
+
+Infer (do not output):
+- When to use vs not use this API
+- Required vs optional parameters
+- Parameter meanings and constraints
+- Cross-parameter dependencies or exclusions
+- Common parameter mistakes
+  - no examples are provided, infer from the schema and baseline description only
+
+Write a clear API description that:
+- States when to use and NOT use the API
+- Does not invent or reference non-provided APIs
+- Explains each parameter's meaning, type, required/optional status, constraints, and defaults
+- Describes likely validation failures and how to avoid them
+- Abstracts patterns into general rules
+- Does not restate the full schema verbatim
+- Does not mention whether examples were provided
+
+You may replace the baseline description entirely.
+
+Output ONLY valid JSON (no markdown, no code blocks):
+{{"description": "<your improved API description here>"}}
+```
+
+### Inputs
+
+- **`tool_name`**: the tool/API name
+- **`parameter_json`**: a JSON string describing the parameter schema (treat this as authoritative)
+- **`original_description`**: the baseline description you want to improve
+
+### Output
+
+The model is trained to output **only valid JSON** with a single field:
+
+- **`description`**: the improved tool description (string)
+
+---
+
+## Prompt variation guidance (SFT-sensitive)
+
+Because this model is SFT to follow a specific prompt and output contract, it can be sensitive to prompt changes. The safest strategy is to treat the prompt as a template and apply only **minimal, well-scoped edits**.
+
+### Prompt invariants (do not change)
+
+- Keep the three input slots exactly: `{tool_name}`, `{parameter_json}`, `{original_description}`
+- Keep: **“Output ONLY valid JSON (no markdown, no code blocks)”**
+- Keep the output schema exactly: `{"description": "..."}` (same key name; no extra keys)
+
+### Safe, minimal edits (usually OK)
+
+- Add 1–3 bullets under **“Infer (do not output)”** to clarify what to pay attention to
+- Add constraints under **“Write a clear API description that:”** as additional bullets
+- Add brief reminders about schema authority, parameter-name exactness, or concision
+
+### Risky edits (often break JSON / behavior)
+
+- Reordering or removing the output-format lines
+- Asking for examples, multi-part outputs, markdown, or extra keys
+- Changing placeholder names or introducing new “inputs” not present during training
+
+### Concrete example: minimal diff that still tends to work
+
+The prompt below is a conservative variation. It adds clarifications without changing the core structure or output contract:
+
+```diff
+ Infer (do not output):
+ - Preserve key lexical tokens from the baseline description that may match user queries
+ - Clarify boundaries if this API could be confused with similar tools
+
+ Write a clear API description that:
+ - Treats the parameter schema as authoritative and does not introduce fields, types, or requirements not defined in it
+ - Explains each parameter's meaning ... while keeping parameter names exactly as defined in the schema
+ - Lists REQUIRED parameters before optional ones
+ - Uses enumerated or candidate values exactly as defined in the schema when applicable
+ - Describes likely validation failures strictly based on schema-defined constraints ...
+ - Keeps the description concise and avoids unnecessary verbosity
+```
+
+---
+
+## Inference
+
+### Option A: Use the `tool-optimizer` library (recommended)
+
+The open-source repo includes a working CLI that runs this model with either **vLLM** or **Hugging Face Transformers**:
+
+```bash
+git clone https://github.com/intuit-ai-research/tool-optimizer
+cd tool-optimizer
+
+# Install (one option)
+python -m pip install -e .
+
+# Run inference (vLLM default)
+python src/agent_tool_optimizer/inference_main.py \
+  --model_name intuit/agent-tool-optimizer \
+  --dataset_id intuit/tool-optimizer-dataset
+```
+
+Notes:
+
+- `--inference_engine vllm` (default) or `--inference_engine hf`
+- The dataset is expected to have a `test` split with a `prompt` field.
+
+### Option B: Transformers (direct)
+
+```python
+import json
+from transformers import pipeline
+import torch
+
+model_id = "intuit/agent-tool-optimizer"
+gen = pipeline(
+    "text-generation",
+    model=model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True,
+)
+
+prompt = """<prompt above>"""
+
+out = gen(
+    [{\"role\": \"user\", \"content\": prompt}],
+    max_new_tokens=512,
+    do_sample=True,
+    temperature=0.6,
+    top_p=0.95,
+    top_k=40,
+    return_full_text=False,
+)
+result = out[0][\"generated_text\"]
+print(result)
+
+# Optional: validate JSON
+json.loads(result)
+```
+
+---
+
+## Example (Before vs After)
+
+![Screenshot 2026-02-20 at 5.23.36 PM](https://cdn-uploads.huggingface.co/production/uploads/65dcb410bda21d181b38321b/dFj0XgXancXD51iyGxC83.png)
+

added_tokens.json

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+{
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+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
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+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
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+  "hidden_act": "silu",
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+  "intermediate_size": 9728,
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+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 262144,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 5000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.57.1",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.57.1"
+}

merge_provenance.json

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+{
+  "base_model": "Qwen/Qwen3-4B-Instruct-2507",
+  "lora_adapter": "/home/sagemaker-user/user-default-efs/lora_adapter",
+  "dtype": "bfloat16",
+  "device_map": "auto",
+  "safe_serialization": true,
+  "max_shard_size": "2GB"
+}

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