# bizidashboard-mcp

> bizidashboard-mcp — gcaguilar-bizidashboard-mcp. Use this tool when you need to access historical and analytical data for the Zaragoza Bizi bike-share system, enabling insights into rankings, occupancy patterns, and mobility signals. It solves problems related to bike-share system optimization, such as station rebalancing and alert monitoring, by providing diagnostic tools and data queries. The tool accepts query inputs and returns relevant data outputs, making it ideal for use cases requiring data-driven decision-making for urban mobility systems.

Canonical page: https://skillsregistry.net/skills/gcaguilar-bizidashboard-mcp  
JSON: https://api.skillsregistry.net/v1/skills/gcaguilar-bizidashboard-mcp

## Description

MCP server that exposes BiziDashboard's historical and analytical data for the Zaragoza Bizi bike-share system as tools for LLM clients, enabling queries on rankings, occupancy patterns, mobility signals, alert history, and station rebalancing diagnostics.

## Trust

- **Trust score (0–1):** 0.69
- **Verification tier:** scanned
- **Last scanned:** 2026-08-29

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-08-29

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/p2fpo84pba)
- **Repository:** <https://github.com/gcaguilar/bizidashboard-mcp>

## Use it

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": "gcaguilar-bizidashboard-mcp"
    }
  }
}
```

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