# MCP Server

> Use this tool when you need to optimize AI agent performance by reducing context window bloat and scaling multi-agent architectures. The MCP Server centralizes memory, tools, and logic, allowing for lean prompts and efficient information management. It takes in distributed agent data as input and outputs a unified, persistent backend for streamlined AI operations.

Canonical page: https://skillsregistry.net/skills/geoaziz-mcp-server  
JSON: https://api.skillsregistry.net/v1/skills/geoaziz-mcp-server

## Description

A persistent backend that reduces AI agent context window bloat by centralizing memory, tools, and logic, enabling lean prompts and scalable multi-agent architectures.

## Trust

- **Trust score (0–1):** 0.59
- **Verification tier:** scanned
- **Last scanned:** 2026-09-03

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-03

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/xzk3sr9kaf)
- **Repository:** <https://github.com/GeoAziz/mcp-server>

## 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": "geoaziz-mcp-server"
    }
  }
}
```

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