# MCP Server + Document Memory System

> Use this tool when you need to enhance AI systems with persistent memory and contextual understanding, solving problems of information recall and request routing. It enables semantic search and quality-scored content synthesis, taking in document data and interaction history as inputs and outputting intelligent responses and routed requests. Ideal for use cases requiring AI systems to learn from and adapt to user interactions over time.

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

## Description

Enables AI systems to remember interactions, understand document context through semantic search, and intelligently route requests with persistent memory and quality-scored content synthesis.

## Trust

- **Trust score (0–1):** 0.69
- **Verification tier:** scanned
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** search
- **Updated:** 2026-09-28

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/azt6cwdh58)
- **Repository:** <https://github.com/MarvelEyiosa/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": "marveleyiosa-mcp"
    }
  }
}
```

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