Massive Context MCP

Install

Works with Claude Code, Codex, VS Code, Claude Desktop, Cursor, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install connector/egoughnour-massive-context-mcp

About

No reviews yet. Installed it? Reviews come from verified installers only. How reviews work

Permissions

What this server can do and what it asks for, from its tool list and install config. Checked Oct 25, 2026.

Tools

9 read · 4 write · 0 delete · 4 not yet classified

Secrets

None asked for

Install scripts

Not scanned yet

Runs as

Python package

Tools (17)

Read from the source code; the running server may differ. Descriptions are the server’s own words; hidden characters are shown as [U+…].

• rlm_auto_analyze

  Automatically detect content type and analyze with optimal chunking strategy. One-step analysis for common tasks. Args: name: Context identifier content: The content to analyze goal: Analysis goal: 'summarize', 'find_bugs', 'extract_structure', 'security_audit', or 'answer:<your question>' provider: LLM provider - 'auto' prefers Ollama if available concurrency: Max parallel requests (default 4, max 8)

• rlm_chunk_context

  Chunk a loaded context by strategy. Returns chunk metadata, not full content. Args: name: Context identifier strategy: Chunking strategy - 'lines', 'chars', or 'paragraphs' size: Chunk size (lines/chars depending on strategy)

• rlm_exec

  Execute Python code against a loaded context in a sandboxed subprocess. Set result variable for output. Args: code: Python code to execute. User sets result variable for output. context_name: Name of previously loaded context timeout: Max execution time in seconds (default 30) Security: When RLM_FIREWALL_ENABLED=1, code is checked against known dangerous patterns before execution. Blocked code returns an error instead of executing.

• rlm_filter_context

  Filter context using regex/string operations. Creates a new filtered context. Args: name: Source context identifier output_name: Name for filtered context pattern: Regex pattern to match mode: 'keep' or 'remove' matching lines

• rlm_firewall_status

  Check the status of the code execution firewall. Returns information about whether the firewall is enabled, the Ollama endpoint being used, and whether dangerous code patterns will be blocked. The firewall is auto-enabled when code-firewall-mcp is installed: pip install massive-context-mcp[firewall] Returns: { "enabled": bool, "package_installed": bool, "ollama_url": str, "embedding_model": str, "similarity_threshold": float, "ollama_reachable": bool, }

• rlm_get_chunk

  Get a specific chunk by index. Use after chunking to retrieve individual pieces. Args: name: Context identifier chunk_index: Index of chunk to retrieve

• rlm_get_results

  Retrieve stored results for aggregation. Args: name: Result set identifier

• rlm_inspect_context

  Inspect a loaded context - get structure info without loading full content into prompt. Args: name: Context identifier preview_chars: Number of chars to preview (default 500)

• rlm_list_contexts

  List all loaded contexts and their metadata.

• rlm_load_context

  Load a large context as an external variable. Returns metadata without the content itself. Args: name: Identifier for this context content: The full context content

• rlm_ollama_status

  Check Ollama server status and available models. Returns whether Ollama is running, list of available models, and if the default model (gemma3:12b) is available. Use this to determine if free local inference is available. Args: force_refresh: Force refresh the cached status (default: false)

• rlm_setup_ollama

  Install Ollama via Homebrew (macOS). Requires Homebrew pre-installed. Uses 'brew install' and 'brew services'. PROS: Auto-updates, pre-built binaries, managed service. CONS: Requires Homebrew, may prompt for sudo on first Homebrew install. Args: install: Install Ollama via Homebrew (requires Homebrew) start_service: Start Ollama as a background service via brew services pull_model: Pull the default model (gemma3:12b) model: Model to pull (default: gemma3:12b). Use gemma3:4b or gemma3:1b for lower RAM systems.

• rlm_setup_ollama_direct

  Install Ollama via direct download (macOS). Downloads from ollama.com to ~/Applications. PROS: No Homebrew needed, no sudo required, fully headless, works on locked-down machines. CONS: Manual PATH setup, no auto-updates, service runs as foreground process. Args: install: Download and install Ollama to ~/Applications (no sudo needed) start_service: Start Ollama server (ollama serve) in background pull_model: Pull the default model (gemma3:12b) model: Model to pull (default: gemma3:12b). Use gemma3:4b or gemma3:1b for lower RAM systems.

• rlm_store_result

  Store a sub-call result for later aggregation. Args: name: Result set identifier result: Result content to store metadata: Optional metadata about this result

• rlm_sub_query

  Make a sub-LLM call on a chunk or filtered context. Core of recursive pattern. Args: query: Question/instruction for the sub-call context_name: Context identifier to query against chunk_index: Optional: specific chunk index provider: LLM provider - 'auto', 'ollama', or 'claude-sdk'. 'auto' prefers Ollama if available (free local inference) model: Model to use (provider-specific defaults apply)

Signals

Stars

1

Forks

0

Open issues

0

Weekly downloads

62

Last commit

Jan 19, 2026 (9 months ago)

Last release

Jan 19, 2026 (9 months ago)

Last package publish

Jan 19, 2026 (9 months ago)

From GitHub (fetched Oct 24, 2026) and the package registry.

Details

Registry name

io.github.egoughnour/massive-context-mcp

Version

3.0.1

Auth

No auth

Packages

pypi massive-context-mcpmcpb https://github.com/egoughnour/massive-context-mcp/releases/download/v3.0.1/massive-context-mcp.mcpb

License

MIT

Repository

github.com/egoughnour/massive-context-mcp

First seen

Oct 24, 2026

Last checked

Oct 24, 2026

Source

Official MCP Registrygithub-graphql