# Elasticsearch Memory MCP

> Use this tool when you need to enhance the memory capabilities of large language models (LLMs) with persistent and intelligent storage, utilizing Elasticsearch for efficient hierarchical categorization and semantic search. It solves problems of knowledge retention and retrieval in LLM contexts, enabling more accurate and informed responses. Ideal for applications requiring advanced memory management and search functionality.

Canonical page: https://skillsregistry.net/skills/fredac100-elasticsearch-memory-mcp  
JSON: https://api.skillsregistry.net/v1/skills/fredac100-elasticsearch-memory-mcp

## Description

Provides persistent, intelligent memory using Elasticsearch with hierarchical categorization and semantic search for LLM contexts.

## Trust

- **Trust score (0–1):** 0.35
- **Verification tier:** scanned
- **Last scanned:** 2026-09-01

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-01

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/ucibyge0u6)
- **Repository:** <https://github.com/fredac100/elasticsearch-memory-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": "fredac100-elasticsearch-memory-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/fredac100-elasticsearch-memory-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/fredac100-elasticsearch-memory-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