How do I install the massive mcp server?

Use a recorded configuration for your client, replace example credential values and paths, and verify the documented runtime. Restart or reconnect the client, then inspect the available tools.

What credentials does it need?

It reads `RLM_DATA_DIR`, `OLLAMA_URL` from the environment. Store these in your client's env block or a secrets manager — never commit them to a repository — and grant the narrowest scope that still covers your use case.

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Massive MCP Server

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

Local serverstdioPython

What is the Massive MCP Server?

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

More MCP options

Compare MCP servers by category, or browse hosted MCP servers if you prefer an endpoint over a local process.

Maintainer setup documentation.

Recorded tools

Check the linked publisher documentation and your connected client's tool list. Names and availability can vary with the server version and account permissions.

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

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

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

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

Handles 10M+ token contexts with chunking, sub-queries, and local Ollama inference. Confirm the tools offered by your installed version in the connected client. Tool availability and permissions depend on the server and account configuration.