전송별 단일 기동: ``` uv run python -m sootool --transport http --port 10535 uv run python -m sootool --transport sse-legacy --port 10536 uv run python -m
전송별 단일 기동: ``` uv run python -m sootool --transport http --port 10535 uv run python -m sootool --transport sse-legacy --port 10536 uv run python -m sootool --transport websocket --port 10537 uv run python -m sootool --transport unix. The sootool mcp server wraps that behind the Model Context Protocol, so an assistant can use it through 14 defined tools rather than through you.
Setup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client.
Everything the assistant can do here goes through one of these:
core.batch — N개 독립 연산을 ThreadPoolExecutor로 병렬 실행. id 중복 거부, per-item 격리(ok|error|timeout|skipped), 최대 500 items, per-item 10s, batch 60s 제한. 결과는 입력 id 순으로 결정적core.pipeline — graphlib.TopologicalSorter 기반 DAG 실행기. ${step_id.result.field} 참조 문법으로 단계 간 데이터 전달. max_steps=50, max_depth=10, step_timeout_s=2.0core.pipeline_resume — 실패한 스텝을 TTL 10분 in-memory 캐시로부터 부분 재실행Decimal — only 경계 · 명시적 rounding · 감사 트레이스 · 모듈 stateless / batch-safeProvider — ModelSooTool — Decimal 커널Google — gemini-2.5-proAnthropic — claude-sonnet-4-5OpenAI — gpt-4oNamespace — Countcore — 8accounting — 11This sits in the AI and media services group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. Sootool's toolset — core.batch, core.pipeline, core.pipeline_resume and 11 more — is a fair guide to whether it matches your workflow. It is maintained by jinho-von-choi; worth a glance at recent repository activity before you build anything load-bearing on it.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
| Tool | What it does |
|---|---|
| core.batch | N개 독립 연산을 ThreadPoolExecutor로 병렬 실행. id 중복 거부, per-item 격리(ok|error|timeout|skipped), 최대 500 items, per-item 10s, batch 60s 제한. 결과는 입력 id 순으로 결정적 재정렬된다(ADR-011). |
| core.pipeline | graphlib.TopologicalSorter 기반 DAG 실행기. ${step_id.result.field} 참조 문법으로 단계 간 데이터 전달. max_steps=50, max_depth=10, step_timeout_s=2.0, pipeline_timeout_s=30.0(ADR-006). |
| core.pipeline_resume | 실패한 스텝을 TTL 10분 in-memory 캐시로부터 부분 재실행. |
| Decimal | only 경계 · 명시적 rounding · 감사 트레이스 · 모듈 stateless / batch-safe |
| Provider | Model |
| SooTool | Decimal 커널 |
| gemini-2.5-pro | |
| Anthropic | claude-sonnet-4-5 |
| OpenAI | gpt-4o |
| Namespace | Count |
| core | 8 |
| accounting | 11 |
| finance | 15 |
| tax | 10 |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.