Handles 10M+ token contexts with chunking, sub-queries, and local Ollama inference.
If you already use Massive, the massive mcp server is the piece that lets your assistant work with it directly. Handles 10M+ token contexts with chunking, sub-queries, and local Ollama inference.
The toolset is worth reading before you wire it up, because it tells you what the integration is really for:
rlm_system_check — Check system requirements — verify macOS, Apple Silicon, 16GB+ RAM, Homebrewrlm_setup_ollama — Install via Homebrew — managed service, auto-updates, requires Homebrewrlm_setup_ollama_direct — Install via direct download — no sudo, fully headless, works on locked-down machinesrlm_ollama_status — Check Ollama availability — detect if free local inference is availablerlm_auto_analyze — One-step analysis — auto-detects type, chunks, and queriesrlm_load_context — Load context as external variablerlm_inspect_context — Get structure info without loading into promptrlm_chunk_context — Chunk by lines/chars/paragraphsrlm_get_chunk — Retrieve specific chunkrlm_filter_context — Filter with regex (keep/remove matching lines)rlm_exec — Execute Python code against loaded context (sandboxed)rlm_sub_query — Make sub-LLM call on chunkConfiguration is passed through the environment: RLM_DATA_DIR, OLLAMA_URL. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.
The server ships on PyPI as massive-context-mcp, so your MCP client can launch it on demand — there is no separate build step. Add the server block to your client's configuration, restart it, and the tools register themselves.
This sits in the browser automation group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. Massive's toolset — rlm_system_check, rlm_setup_ollama, rlm_setup_ollama_direct and 11 more — is a fair guide to whether it matches your workflow. It is maintained by egoughnour; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against Massive's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
| Tool | What it does |
|---|---|
| rlm_system_check | **Check system requirements** — verify macOS, Apple Silicon, 16GB+ RAM, Homebrew |
| rlm_setup_ollama | **Install via Homebrew** — managed service, auto-updates, requires Homebrew |
| rlm_setup_ollama_direct | **Install via direct download** — no sudo, fully headless, works on locked-down machines |
| rlm_ollama_status | **Check Ollama availability** — detect if free local inference is available |
| rlm_auto_analyze | **One-step analysis** — auto-detects type, chunks, and queries |
| rlm_load_context | Load context as external variable |
| rlm_inspect_context | Get structure info without loading into prompt |
| rlm_chunk_context | Chunk by lines/chars/paragraphs |
| rlm_get_chunk | Retrieve specific chunk |
| rlm_filter_context | Filter with regex (keep/remove matching lines) |
| rlm_exec | Execute Python code against loaded context (sandboxed) |
| rlm_sub_query | Make sub-LLM call on chunk |
| rlm_sub_query_batch | Process multiple chunks in parallel |
| rlm_store_result | Store sub-call result for aggregation |
{
"mcpServers": {
"massive-context": {
"command": "uvx",
"args": ["massive-context-mcp"],
"env": {
"RLM_DATA_DIR": "your-value",
"OLLAMA_URL": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
| Variable | Description | Required |
|---|---|---|
| RLM_DATA_DIR | Filesystem location the server is allowed to use. | Optional |
| OLLAMA_URL | Endpoint or connection string the server talks to. | Yes |
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