| title | Tool Call Parsing (Dynamo) |
|---|---|
| subtitle | Connect Dynamo to external tools and services using Dynamo's built-in tool call parsers |
You can connect Dynamo to external tools and services using tool calling. By providing a list of available functions, Dynamo can choose to output function arguments for the relevant function(s) which you can execute to augment the prompt with relevant external information.
Tool calling is controlled using the tool_choice and tools request
parameters.
This page covers parser names for the default Dynamo-native path. If Dynamo
does not list a parser for your model, see
Parser Engine Fallback. For how
--dyn-tool-call-parser combines with --dyn-chat-processor and
--dyn-reasoning-parser (and which combinations are invalid), see
Parser Configuration.
To enable this feature, you should set the following flag while launching the backend worker
--dyn-tool-call-parser: select the tool call parser from the supported list below
# <backend> can be sglang, trtllm, vllm, etc. based on your installation
python -m dynamo.<backend> --helpTip
If your model's default chat template doesn't support tool calling, but the model itself does, you can specify a custom chat template per worker
with python -m dynamo.<backend> --custom-jinja-template </path/to/template.jinja>.
Tip
If your model also emits reasoning content that should be separated from normal output, see Reasoning Parsing (Dynamo) for the supported --dyn-reasoning-parser values.
The table below lists the currently supported tool call parsers in Dynamo's registry. The
Upstream name column shows where the vLLM or SGLang parser name differs
from Dynamo's -- relevant when using --dyn-chat-processor vllm or sglang
(see Parser Engine Fallback). A blank upstream
column means the same name works everywhere. Dynamo-only means no upstream
parser exists for this format.
| Parser Name | Models | Upstream name | Notes |
|---|---|---|---|
kimi_k2 |
Kimi K2 Instruct/Thinking, Kimi K2.5 | Pair with --dyn-reasoning-parser kimi or kimi_k25 |
|
minimax_m2 |
MiniMax M2 / M2.1 | vLLM: minimax |
XML <minimax:tool_call> |
deepseek_v4 |
DeepSeek V4 Pro / Flash | vLLM: deepseek_v4; SGLang: deepseekv4 |
DSML tags (<|DSML|tool_calls>...). Aliases: deepseek-v4, deepseekv4 |
deepseek_v3 |
DeepSeek V3, DeepSeek R1-0528+ | SGLang: deepseekv3 |
Special Unicode markers |
deepseek_v3_1 |
DeepSeek V3.1 | Dynamo-only | JSON separators |
deepseek_v3_2 |
DeepSeek V3.2+ | Dynamo-only | DSML tags (<|DSML|function_calls>...) |
qwen3_coder |
Qwen3.5, Qwen3-Coder | XML <tool_call><function=...> |
|
glm47 |
GLM-4.5, GLM-4.7 | Dynamo-only | XML <arg_key>/<arg_value> |
nemotron_deci |
Nemotron-Super / -Ultra / -Deci, Llama-Nemotron-Ultra / -Super | Dynamo-only | <TOOLCALL> JSON |
nemotron_nano |
Nemotron-Nano | Dynamo-only | Alias for qwen3_coder |
gemma4 |
Google Gemma 4 (thinking models) | vLLM: gemma4 |
Custom non-JSON grammar with <|"|> string delimiters and <|tool_call>...<tool_call|> markers. Aliases: gemma-4. Pair with --dyn-reasoning-parser gemma4 and --custom-jinja-template examples/chat_templates/gemma4_tool.jinja |
harmony |
gpt-oss-20b / -120b | Dynamo-only | Harmony channel format |
hermes |
Qwen2.5-*, QwQ-32B, Qwen3-Instruct, Qwen3-Think, NousHermes-2/3 | vLLM: qwen2_5; SGLang: qwen25 (for Qwen models) |
<tool_call> JSON |
phi4 |
Phi-4, Phi-4-mini, Phi-4-mini-reasoning | vLLM: phi4_mini_json |
functools[...] JSON |
pythonic |
Llama 4 (Scout / Maverick) | Python-list tool syntax | |
llama3_json |
Llama 3 / 3.1 / 3.2 / 3.3 Instruct | <|python_tag|> tool syntax |
|
mistral |
Mistral / Mixtral / Mistral-Nemo, Magistral | [TOOL_CALLS]...[/TOOL_CALLS] |
|
jamba |
Jamba 1.5 / 1.6 / 1.7 | Dynamo-only | <tool_calls> JSON |
default |
(fallback) | Dynamo-only | Empty JSON config (no start/end tokens). Prefer a model-specific parser for production use. |
Tip
For Kimi K2.5 thinking models, pair --dyn-tool-call-parser kimi_k2 with
--dyn-reasoning-parser kimi_k25 from Reasoning Parsing (Dynamo) so that both <think> blocks and tool calls
are parsed correctly from the same response.
# launch backend worker (or dynamo.vllm)
python -m dynamo.sglang --model Qwen/Qwen3.5-4B --dyn-tool-call-parser qwen3_coder --dyn-reasoning-parser qwen3
# launch frontend worker
python -m dynamo.frontendcurl -s http://localhost:8000/v1/chat/completions \
-H 'Content-Type: application/json' \
-d '{
"model": "Qwen/Qwen3.5-4B",
"messages": [
{"role": "user", "content": "What is the weather in San Francisco and New York?"}
],
"tools": [{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather for a location.",
"parameters": {
"type": "object",
"properties": {"location": {"type": "string"}},
"required": ["location"]
}
}
}],
"tool_choice": "auto"
}'Dynamo parses the tool calls out of the model output and surfaces them as
OpenAI-compatible tool_calls entries on the response:
{
"id": "chatcmpl-b415caad-9be0-4d9e-ac6d-9d23bfe57703",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": null,
"reasoning_content": "The user is asking about the weather in two cities: San Francisco and New York. I need to call the get_weather function for each city. I'll make two separate function calls to get the weather information for both locations.\n",
"tool_calls": [
{
"id": "call-56223a95-3d14-4433-a94e-011f106c0e40",
"type": "function",
"function": {
"name": "get_weather",
"arguments": "{\"location\":\"San Francisco\"}"
}
},
{
"id": "call-d5b5772b-6b0c-4120-ad01-623278a937fe",
"type": "function",
"function": {
"name": "get_weather",
"arguments": "{\"location\":\"New York\"}"
}
}
]
},
"finish_reason": "tool_calls",
"logprobs": null
}
],
"created": 1778653281,
"model": "Qwen/Qwen3.5-4B",
...
}Tip
If a tool call comes back wrong, add "logprobs": true to a single repro
request and share the response. See
Troubleshooting Tool Calls for what to capture and
include when reporting an issue.
You can optionally turn on xgrammar structural tags so guided decoding matches the parser's tool-call format at token granularity. See Structural tag (guided decoding for tool calls).