# Massive Context

> Use this tool when you need to process and analyze extremely large text contexts, solving problems such as information overload and context fragmentation. It takes in massive texts (10M+ tokens) and outputs synthesized results, supporting both local and cloud-based inference. Ideal for use cases requiring deep understanding of vast amounts of text data, such as research, data mining, and knowledge graph construction.

Canonical page: https://skillsregistry.net/skills/egoughnour-massive-context  
JSON: https://api.skillsregistry.net/v1/skills/egoughnour-massive-context

## Description

Processes extremely large text contexts (10M+ tokens) using the Recursive Language Model pattern. Strategically chunks content, runs sub-queries on individual chunks, and aggregates results for final synthesis. Supports local inference via Ollama and cloud fallback via Claude SDK, with tools for auto-analysis, context loading, batch sub-queries, and sandboxed Python code execution against loaded context.

## Trust

- **Trust score (0–1):** 0.50
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** other
- **Updated:** 2026-04-25

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/egoughnour-massive-context)
- **Repository:** <https://github.com/egoughnour/massive-context-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": "egoughnour-massive-context"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/egoughnour-massive-context` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/egoughnour-massive-context/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