---

name: structured-tool-reasoning description: "Use this vLLM sub-skill for structured outputs, JSON/regex/grammar constraints, tool calling, reasoning parsers, chat-template/tool-parser routing, streaming tool-call deltas, and parser/backend troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Structured Tool Reasoning

Use this sub-skill when the task involves constrained generation, OpenAI-compatible response_format or structured_outputs, tool/function calling, reasoning parser output, or debugging parser/template/backend behavior in vLLM.

Route by Task

• For JSON schema, JSON object, choice, regex, grammar, or structural-tag constraints, read references/structured-outputs.md.
• For OpenAI-compatible tools, tool_choice, auto tool choice, strict tool schemas, parser names, chat templates, and streaming tool-call assembly, read references/tool-calling.md.
• For --reasoning-parser, reasoning fields, reasoning streaming deltas, thinking controls, and combining reasoning with tools or structured outputs, read references/reasoning-parsers.md.
• For unsupported schema keywords, invalid grammars, backend dependency issues, parser name mismatches, incompatible chat templates, missing streamed tool calls, or absent reasoning content, read references/troubleshooting.md.

Safe Local Helper

Use the bundled validator before contacting a server when a user provides a request fragment:

# From this sub-skill directory:
python scripts/validate_structured_request.py --input request.json
python scripts/validate_structured_request.py --example tool-streaming

# From the root vLLM skill directory:
python sub-skills/structured-tool-reasoning/scripts/validate_structured_request.py --input request.json

The helper checks request-shape consistency for response_format, structured_outputs, tools, tool_choice, reasoning flags, common backend compatibility risks, and streaming tool-call handling. It does not import vLLM, download models, start a server, execute tools, or call external APIs.

Boundaries

• Route general server startup, authentication, host/port, OpenAI endpoint coverage, and deployment layout to ../openai-serving/ when present.
• Route basic LLM.generate or LLM.chat setup without structured constraints to ../offline-inference/ when present.
• Route multimodal, pooling, embeddings, adapters, and LoRA-specific requests to ../modalities-adapters-pooling/ when present.
• Route performance tuning of structured decoding backends, throughput, batching, and benchmark questions to ../deployment-performance/ when present.

Hardware and Runtime Notes

• Offline and serving examples require user-provided models and hardware appropriate for the selected model; CPU-only or precompiled inspection environments prove imports and signatures, not GPU throughput.
• OpenAI-compatible examples assume the user already has a running vLLM server exposing /v1.
• Tool execution is always application-owned: vLLM returns tool-call names and argument strings; caller code validates and executes the actual functions.