Massive MCP Server

Handles 10M+ token contexts with chunking, sub-queries, and local Ollama inference.

Local serverstdioPython

What is the Massive MCP server?

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.

Available tools

The toolset is worth reading before you wire it up, because it tells you what the integration is really for:

  • rlm_system_checkCheck system requirements — verify macOS, Apple Silicon, 16GB+ RAM, Homebrew
  • rlm_setup_ollamaInstall via Homebrew — managed service, auto-updates, requires Homebrew
  • rlm_setup_ollama_directInstall via direct download — no sudo, fully headless, works on locked-down machines
  • rlm_ollama_statusCheck Ollama availability — detect if free local inference is available
  • rlm_auto_analyzeOne-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

Credentials and setup notes

Configuration 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.

Installation

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.

Where it fits

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.

Worth knowing first

  • It runs with your machine's permissions. That is convenient and also the reason to think about what you point it at before you approve a tool call.
  • With 14 tools registered it takes up a noticeable share of the context window; turn it off in projects that never touch Massive.
  • Missing credentials fail quietly in some clients — if no tools show up, check the environment block first.
  • Keep per-call confirmation enabled while you learn its behaviour; it is the cheapest safeguard you have.

Available tools

ToolWhat 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_contextLoad context as external variable
rlm_inspect_contextGet structure info without loading into prompt
rlm_chunk_contextChunk by lines/chars/paragraphs
rlm_get_chunkRetrieve specific chunk
rlm_filter_contextFilter with regex (keep/remove matching lines)
rlm_execExecute Python code against loaded context (sandboxed)
rlm_sub_queryMake sub-LLM call on chunk
rlm_sub_query_batchProcess multiple chunks in parallel
rlm_store_resultStore sub-call result for aggregation

How to install the Massive MCP server

{
  "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.

Configuration

VariableDescriptionRequired
RLM_DATA_DIRFilesystem location the server is allowed to use.Optional
OLLAMA_URLEndpoint or connection string the server talks to.Yes

Example prompts to try

  • Use Massive to rlm system check.
  • Use Massive to rlm setup ollama.
  • Use Massive to rlm setup ollama direct.

Frequently asked questions

It connects Massive to MCP-compatible AI assistants such as Claude and Cursor, exposing 14 tools (rlm_system_check, rlm_setup_ollama, rlm_setup_ollama_direct, and more) that the assistant can call on your behalf. Instead of copying data back and forth by hand, the assistant works with Massive directly.