# @memharness/mcp

> @memharness/mcp — las7-memharness. Use this tool when you need to manage complex, time-sensitive data with transparency and accountability. It solves problems related to data storage, recall, and revision, particularly in applications requiring audit trails and provenance tracking. With SQLite storage, it accepts factual inputs and outputs recalled data, revised information, and audit logs, making it ideal for use cases involving temporal data management and knowledge graph updates.

Canonical page: https://skillsregistry.net/skills/las7-memharness  
JSON: https://api.skillsregistry.net/v1/skills/las7-memharness

## Description

A bi-temporal, provenance-carrying memory primitive for AI agents. Enables storing facts, recall, revision, and audit trails via MCP with SQLite storage.

## Trust

- **Trust score (0–1):** 0.68
- **Verification tier:** scanned
- **Last scanned:** 2026-09-03

## Facts

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

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/m76thevxwb)
- **Repository:** <https://github.com/las7/memharness>

## 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": "las7-memharness"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/las7-memharness` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/las7-memharness/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