
Zelinqa MCP
io.github.Zelinqa由 GitHub 账号 Zelinqa 发布v1.0.1更新于 Oct 24, 2026
Goal-oriented conversations and next-question selection through the Zelinqa API.
概览
让助手运行在 Zelinqa 中定义的目标导向对话:开始对话、传入回复、获取下一个问题并记录结果。
- 可以做什么
- 开始或恢复指定名称的对话,返回首个或待回答的问题
- 记录用户回复并返回下一个问题,无回复时仅重新显示待答问题
- 在不消耗轮次的情况下添加上下文,或写入已确认的数据与目标覆盖
- 刷新进度与待答问题,并记录观察到的业务结果
- 释放本地对话句柄
- 适用场景
- 适用于助手必须先询问再决策的场景,例如访谈、线索资格判断、方向引导、分诊或推荐,且问题应来自已发布的 Zelinqa 域而非临时生成。
- 运行要求
- 以 uvx 运行的本地进程,需要 Python 3.11+ 与 uv。需要一个在 Zelinqa Studio 中为某个已发布域创建的运行时 API 密钥(免费 Developer 版账户即可),通过环境变量 ZELINQA_API_KEY 提供。可选变量可设置基础地址、超时、重试与会话恢复。需要访问 Zelinqa API 的网络。
安装
在 SourceWeft 中
- 打开 控制台中的 Zelinqa MCP,将其添加到工作区。
- 为需要使用其工具的对话启用该服务。
Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。
其他 MCP 客户端
参照 仓库 中的启动说明。
README
[PyPI version] [CI] [Python 3.11+] [MCP registry name] [License Apache-2.0]
Install · Example · Tools · Configuration · Operations · Français
Your assistant handles the conversation. Zelinqa decides what to ask next. Describe once, in Zelinqa Studio, what a conversation must find out. This server gives any MCP host the tools to run that conversation: start it, pass each reply, get the next question, record the result. The model only ever sees question text, choice labels and a conversation name. Session, decision and state identifiers stay inside the server.
[An MCP host calls the zelinqa-mcp server over stdio; the server calls the Zelinqa API with the Python SDK and returns question text, choice labels and progress.]Fewer tokens, deterministic, explainable
[70 percent fewer tokens, measured on 700+ conversations; deterministic: the same state returns the same question and choosing never calls a model; explainable: every question says what it collects and progress is reported per dimension.]The engine chooses the questions, so the model stops generating them and stops re-asking: fewer tokens for the same dialogue. The choice itself is a selection over your published domain, not a generation: the same state always yields the same question, and each decision carries what the question collects, the progress of every dimension and an explicit stop reason, so a conversation can be audited after the fact. Token figures: internal test bench, September 2026: more than 700 conversations replayed twice with the same assistant, alone then steered by the Zelinqa engine, with simulated respondents replayed identically on both sides. Protocol, figures and limits: read the test bench report. A scientific benchmark with shared data and code is planned before the end of 2026.
Why
[Without Zelinqa, the model generates every question and decides with gaps. With Zelinqa, the engine runs the questioning phase from the published domain and the model decides once, with a complete typed state.]Any assistant that has to ask before it decides, for an interview, a qualification, an orientation, a triage or a recommendation, improvises its questions and decides with gaps. With this server, the engine runs the questioning phase from your published domain and the model decides once, with a complete, typed state.
Install
You need Python 3.11+ with uv, and a runtime API key for a published domain, created in Zelinqa Studio. A free Developer edition account is enough.
[Install in Cursor] [Install in VS Code]
Or add the server to your host by hand. The command is the same everywhere: uvx zelinqa-mcp, with the key in the host's secret environment.
Ready-to-copy files for each host are in examples/. Keep your host's tool-approval policy: some calls create sessions and feedback.
A conversation in four tool calls
The model calls zelinqa_start, asks the returned question in its own words, passes the person's reply to zelinqa_next_question, and repeats until the engine stops. At the end it records what really happened.
Calling zelinqa_next_question without a reply only redisplays the pending question. When a CRM already knows an answer, zelinqa_adjust records it without asking. zelinqa_adjust and zelinqa_add_context can replace the pending question, for example when the information it asked for is now known: always ask the question they return. The full answering rules are in docs/operations.md.
Tools
Results carry question text, ranks, choice labels, objective progress and counters; next-decision results add warnings, the stop reason and degraded-mode reasons. At max_turns the result has action: "stop" and stop_reason: "max_turns_reached", and later calls return the same stop. Reaching the turn limit is not proof that the objective was achieved.
zelinqa-mcp --advanced swaps the business tools for six low-level ones that expose API identifiers and full state. See docs/operations.md.
Configuration
Security and state
- Transport is stdio, one isolated process per trusted host and user. HTTP hosting is disabled until authentication and session isolation exist. Logs go to stderr; stdout is reserved for MCP.
- The server holds at most 128 named conversations in memory and never evicts one silently. Names are process-local: to survive a restart, the host keeps the API session id and injects the resume variables above, outside the model.
- Calls that mutate the same conversation must be sequential; a simultaneous call is rejected rather than answered with stale state. After a conflict, call
zelinqa_statusand reconcile before answering again.
Details, retry semantics and the test suites: docs/operations.md. Vulnerability reports: SECURITY.md.
Related
- zelinqa-sdk: the Python and TypeScript clients this server is built on, for applications that call the API directly.
- docs.zelinqa.ai: concepts, Studio guide and API reference.
License
Apache-2.0. Release steps are in RELEASING.md.
来源:README.md,提交 98537b8
工具
0版本历史
2- v1.0.1最新Sep 29, 2026
- v1.0.0Sep 28, 2026
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