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Zelinqa MCP

Zelinqa MCP

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docs.zelinqa.aiAI & Agents1Updated 2w ago

About Zelinqa MCP

Zelinqa MCP connects MCP hosts to Zelinqa's API for goal-oriented conversations. The API selects deterministic next questions from a published domain, tracks progress, and returns typed results for review or audit.

Key features

  1. 01Start and resume named conversations
  2. 02Select deterministic next questions
  3. 03Apply known answers and context
  4. 04Track objective progress and stop reasons
  5. 05Record observed business outcomes

Use cases

  1. 01Collect information for lead qualification
  2. 02Run structured interview or triage conversations
  3. 03Use known CRM data to skip questions

MCP support

The server exposes Zelinqa conversation tools, a guide resource, and an integration-check prompt to MCP hosts. It sends replies and conversation requests to the Zelinqa API using a runtime API key.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"zelinqa-zelinqa": {
"command": "uvx",
"args": [
"zelinqa-mcp"
],
"env": {
"ZELINQA_API_KEY": "<ZELINQA_API_KEY>"
}
}
}
}
Run commanduvx zelinqa-mcp
Environment variables to set
ZELINQA_API_KEYSecretRuntime key for a published Zelinqa domain, supplied by the host.

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
pypizelinqa-mcp1.0.1

Tools

7

Saved tool list · updated 3w ago

ParameterTypeRequiredDescription
conversationstringYesA unique business name for this conversation, not a session ID. No personal data.

Details

From the project README

Zelinqa MCP

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.

Claude CodeClaude Desktop · Cursor · Windsurf
claude mcp add zelinqa \
  -e ZELINQA_API_KEY=YOUR_RUNTIME_API_KEY \
  -- uvx zelinqa-mcp
{
  "mcpServers": {
    "zelinqa": {
      "command": "uvx",
      "args": ["zelinqa-mcp"],
      "env": {
        "ZELINQA_API_KEY": "YOUR_RUNTIME_API_KEY"
      }
    }
  }
}
Codex (~/.codex/config.toml)From a checkout
[mcp_servers.zelinqa]
command = "uvx"
args = ["zelinqa-mcp"]
env = { ZELINQA_API_KEY = "YOUR_RUNTIME_API_KEY" }
git clone https://github.com/Zelinqa/zelinqa-mcp.git
cd zelinqa-mcp
uv sync --group dev --locked
uv run zelinqa-mcp --version

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.

▶ zelinqa_start           {"conversation": "lead-42"}
◀ ask  ·  "Which budget range are you considering this year?"
          choices: Under €10k · €10k to €50k · Over €50k

▶ zelinqa_next_question   {"conversation": "lead-42", "choice_labels": ["€10k to €50k"]}
◀ ask  ·  "When would you like to start?"
          progress: budget covered · timeline not started

▶ zelinqa_next_question   {"conversation": "lead-42", "user_text": "Next quarter, after the sales kickoff."}
◀ stop ·  objective achieved

▶ zelinqa_feedback        {"conversation": "lead-42", "result": "success"}

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

ToolWhat it does
zelinqa_startStart or recover a named conversation and return its first or pending question
zelinqa_next_questionRecord the person's reply and return the next question; without a reply, redisplay the pending question without a new turn
zelinqa_add_contextAdd a context summary without asking a question
zelinqa_adjustApply confirmed data, dimension statuses or an objective override without consuming a turn
zelinqa_statusRefresh progress and the pending question
zelinqa_feedbackRecord an observed business result: success, partial or failure
zelinqa_forgetFree the local handle; does not delete API data

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.

Also availablePurpose
Resource zelinqa://guideThe same guide supplied as the server's instructions
Prompt zelinqa_integration_checkUser-selected integration test checklist
Skill zelinqaShort workflow for hosts that support skills; copy the directory into your host's skills folder

zelinqa-mcp --advanced swaps the business tools for six low-level ones that expose API identifiers and full state. See docs/operations.md.

Configuration

Environment variablePurpose
ZELINQA_API_KEYRequired runtime key, scoped to one published domain
ZELINQA_BASE_URLDefault https://api.zelinqa.ai
ZELINQA_TIMEOUT_SECONDSPer-attempt timeout, default 30 seconds
ZELINQA_MAX_RETRIESRetry count, default 2
ZELINQA_SESSION_IDOptional host-owned session to resume after restart
ZELINQA_CONVERSATIONLocal name of that resumed session; default conversation

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_status and 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.

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.