# Zentrik

> Zentrik — ai-zentrik-mcp. Use this tool when you need to provide AI agents with contextual product information, including evidence, opportunities, initiatives, and decisions, to inform their decision-making and problem-solving capabilities. It solves problems related to data-driven product development, market analysis, and strategic planning by offering a centralized interface for inputting and outputting relevant data. This tool is ideal for use cases where AI agents require structured product context to generate insights and recommendations.

Canonical page: https://skillsregistry.net/skills/ai-zentrik-mcp  
JSON: https://api.skillsregistry.net/v1/skills/ai-zentrik-mcp

## Description

Product context for AI agents: evidence, opportunities, initiatives, and decisions.

## Trust

- **Trust score (0–1):** 0.30
- **Verification tier:** unverified
- **Last scanned:** 2026-09-02

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-02

## Source

- **Source listing:** [MCP Registry](https://registry.modelcontextprotocol.io/v0/servers/ai.zentrik%2Fmcp)

## Use it

MCP endpoint published by the skill: `https://zentrik.ai/mcp`

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "ai-zentrik-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/ai-zentrik-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/ai-zentrik-mcp/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt