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Massive Context MCP server

by egoughnour·io.github.egoughnour/massive-context-mcp·v3.0.1·1 stars

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

C60/100grade C
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Safety scan
C60/100

full report

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1 stars

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If you have run it, two minutes of your experience saves the next person an afternoon.

Massive Context tools (17, 1 write)

write = sends, deletes, buys or posts

Read from the package source without running it. The installed server may list more.

  • rlm_auto_analyze

    Automatically detect content type and analyze with optimal chunking strategy.

  • rlm_chunk_context

    Chunk a loaded context by strategy. Returns chunk metadata, not full content.

  • rlm_execwrite action

    Execute Python code against a loaded context in a sandboxed subprocess.

  • rlm_filter_context

    Filter context using regex/string operations. Creates a new filtered context.

  • rlm_firewall_status

    Check the status of the code execution firewall.

  • rlm_get_chunk

    Get a specific chunk by index. Use after chunking to retrieve individual pieces.

  • rlm_get_results

    Retrieve stored results for aggregation.

  • rlm_inspect_context

    Inspect a loaded context - get structure info without loading full content into prompt.

  • rlm_list_contexts

    List all loaded contexts and their metadata.

  • rlm_load_context

    Load a large context as an external variable.

  • rlm_ollama_status

    Check Ollama server status and available models.

  • rlm_setup_ollama

    Install Ollama via Homebrew (macOS).

  • rlm_setup_ollama_direct

    Install Ollama via direct download (macOS).

  • rlm_store_result

    Store a sub-call result for later aggregation.

  • rlm_sub_query

    Make a sub-LLM call on a chunk or filtered context. Core of recursive pattern.

  • rlm_sub_query_batch

    Process multiple chunks in parallel. Respects concurrency limit to manage system resources.

  • rlm_system_check

    Check if system meets requirements for Ollama with gemma3:12b.

Public scan report

scanner v0.1.10 · 2026-10-01 · same rubric, same numbers if you re-run it

2 medium
  • Code scan7 source files scanned15/25
  • –Live reliabilityno gateway calls yet and no remote to proben/a
  • –Tool poisoningtools not inspected (local package is not executed); not countedn/a
  • Auth qualitylocal package, no credentials required12/15
  • Maintenancelast push 249 days ago4/15
  • Maintainer identityregistry namespace matches repository owner; GitHub account older than a year8/10

Findings (2)

  • mediumsubprocess with shell=Trueexec.shell-true
    massive_context_mcp-3.0.1/src/rlm_mcp_server.py: … "Process spawning"), ("shell=True", "Shell execution enabled"), …
  • mediumeval / new Function usedexec.eval
    massive_context_mcp-3.0.1/src/rlm_mcp_server.py: …bprocess execution"), ("eval(", "Dynamic code evaluation"), …
Overall 60/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

Install Massive Context in Claude Code, Cursor or VS Code

claude mcp add massive-context-mcp -- uvx massive-context-mcp
Add to Cursor

Massive Context: common questions

Is Massive Context MCP server safe?
With care: it is graded C, so read the findings first (60/100). Read the Massive Context safety report
How do I install Massive Context?
It runs on your machine. Copy the Claude Code, Cursor, VS Code or Claude Desktop config from the install section.
Does Massive Context need an API key?
No secret keys are declared. It reads 4 settings from the environment.
Is Massive Context maintained?
The last commit was 261 days ago (2026-01-25). The latest release is v3.0.1.
What can I use instead of Massive Context?
Servers from other publishers that do the same job: Codebase Memory MCP server.

Alternatives to Massive Context

Same job from other publishers: the closest match first, then the best rated.

  • Codebase Memory
    Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
    C

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