# Zelinqa MCP [Health: Active]

**Category:** 💻 Developer Tools  
**Repository:** https://github.com/Zelinqa/zelinqa-mcp  
**GitHub Stars:** 1  
**Views:** 0  
**Installs:** 0  
**Upvotes:** 0  
**Directory Page:** https://allmcps.com/mcp/zelinqa-mcp

## Description
Goal-oriented conversations and next-question selection through the Zelinqa API.

## Claude Desktop Quick Installation
Install path detected from listing signals. Uses `uvx` (confidence: high):

```json
"mcpServers": {
  "zelinqa-mcp": {
    "command": "uvx",
    "args": ["zelinqa-mcp"],
    "env": {
      "ZELINQA_API_KEY": ""
    }
  }
}
```

**Requires environment variables:** `ZELINQA_API_KEY` — the values above are empty placeholders; fill in real credentials before running (see the repository for what each one is for).

## Documentation

## What Zelinqa MCP MCP server does

Zelinqa MCP MCP server gives an MCP-compatible host a tool-driven way to run conversations defined in Zelinqa Studio. The domain determines what the conversation needs to learn and which question should come next. The assistant presents the returned question in its own words, while the server keeps the conversation identifiers and state.

The server is intended for flows where an assistant must ask questions before making a decision. Possible applications described by the project include interviews, qualification, orientation, triage, and recommendations. Results can include the current question, answer choices, progress by objective dimension, counters, warnings, and a reason for stopping.

## How it works

A typical interaction begins with `zelinqa_start`, using a caller-provided conversation name. The server returns the first or currently pending question. The host then sends a person’s response to `zelinqa_next_question`, using free text or selected choice labels. The same tool returns the next question or indicates that the objective is complete.

Calling `zelinqa_next_question` without a response displays the pending question again without advancing the conversation. Existing information can be added without asking another question through `zelinqa_add_context` or `zelinqa_adjust`. `zelinqa_status` refreshes the current progress and pending question. When the conversation ends, `zelinqa_feedback` records whether the observed business result was a success, partial result, or failure.

Question selection is performed by the Zelinqa engine rather than generated by the language model. The project describes the selection as deterministic for the same state, with progress and stop information included in decision results. At the configured maximum turn count, the result stops with the `max_turns_reached` reason.

## Setup and configuration

The Zelinqa MCP MCP server requires Python 3.11 or newer, `uv`, and a runtime API key for a published domain. The key is created in Zelinqa Studio; the README says a Developer edition account is sufficient. Store the key in the host’s secret environment configuration rather than embedding it in conversation data.

The standard stdio command is:

```bash
uvx zelinqa-mcp
```

Set the required environment variable before starting the process:

```text
ZELINQA_API_KEY=YOUR_RUNTIME_API_KEY
```

The README provides setup examples for Claude Desktop, Cursor, Windsurf, Claude Code, VS Code, and Codex. A source checkout can be prepared with `uv sync --group dev --locked`, followed by `uv run zelinqa-mcp --version`.

## Tools and capabilities

The available tools are:

- `zelinqa_start`: Start or recover a named conversation and return its first or pending question.
- `zelinqa_next_question`: Submit a reply and obtain the next question, or redisplay the pending question without a reply.
- `zelinqa_add_context`: Add a context summary without asking a question.
- `zelinqa_adjust`: Apply confirmed data, dimension statuses, or an objective override without using a turn.
- `zelinqa_status`: Refresh progress and the pending question.
- `zelinqa_feedback`: Record a success, partial, or failure outcome.
- `zelinqa_forget`: Release the local handle without deleting data held by the API.

The model receives question text, choice labels, and conversation naming information. Session, decision, and state identifiers remain inside the server. This design is relevant when the host should handle dialogue presentation while Zelinqa controls question selection and state progression.

_Full upstream README: https://allmcps.com/mcp/zelinqa-mcp/readme_