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Headroom MCP Server

Local-first context compression MCP server for reducing tool outputs, logs, files, RAG chunks, and agent context before they reach the model.

by Tejas Chopra·added 2026-06-05·
Claude CodeClaude Desktop
HarnessClaude CodeClaude Desktop
Review first — review before installing

Open the source and read safety notes before installing.

Safety notes

  • Compression can omit details from the active model context; use retrieval tools when exact logs, code, or evidence matter.
  • Do not treat compressed summaries as authoritative for security, legal, financial, or incident-response decisions without checking originals.
  • Local memory and reversible compression stores should be protected, purged, or excluded from backups when they contain sensitive data.
  • Evaluate compression behavior on your own task types before relying on it for critical workflows.

Privacy notes

  • Tool outputs, logs, source files, prompts, RAG chunks, and conversation context may be stored locally for compression and retrieval.
  • Local stores can include secrets, customer data, stack traces, repository paths, and proprietary code unless filtered upstream.
  • If using proxy or provider integrations beyond local MCP tools, review which data is forwarded to external model providers.

Prerequisites

  • Python 3.10 or newer.
  • Headroom package installed with MCP support.
  • Local disk space for reversible compression, memory, and retrieval stores.
  • MCP client such as Claude Code, Codex, Cursor, Aider, or another compatible host.
  • Team agreement on what content can be compressed, stored, and retrieved by an agent.

Schema details

Install type
cli
Troubleshooting
No
Source repository stats
Scope
Source repo
Collection metadata
Estimated setup
10 minutes
Difficulty
intermediate
Full copyable content
{
  "mcpServers": {
    "headroom": {
      "command": "headroom",
      "args": ["mcp", "serve"]
    }
  }
}

About this resource

Content

Headroom is a context compression layer for AI agents. Its MCP mode exposes tools for compressing large inputs, retrieving originals when needed, and inspecting savings. The project is designed for tool outputs, logs, files, RAG chunks, and conversation history in coding-agent workflows.

The repository describes Headroom as local-first and reversible: originals are stored locally so a model can retrieve more detail when a compressed result is not enough.

Source Review

These sources were reviewed on 2026-06-05. Prefer live docs for current MCP commands, package extras, supported agents, storage behavior, and retrieval semantics.

Features

  • MCP tools for compression, retrieval, and savings stats.
  • Compression for logs, tool outputs, files, RAG chunks, code, JSON, and prose.
  • Reversible compression with local storage of originals.
  • Agent wrappers for Claude Code, Codex, Cursor, Aider, Copilot CLI, and OpenClaw.
  • Proxy and SDK modes for non-MCP integrations.
  • Cross-agent memory and deduplication features.

Installation

Install the Python package with MCP support:

pip install "headroom-ai[mcp]"
headroom mcp install

Use the current project docs for the exact MCP install or serve command for your client. A typical stdio server configuration shape is:

{
  "mcpServers": {
    "headroom": {
      "command": "headroom",
      "args": ["mcp", "serve"]
    }
  }
}

Use Cases

  • Compress large command output before it fills the model context window.
  • Let an agent inspect logs while keeping originals retrievable.
  • Reduce repeated RAG chunks or file reads during codebase exploration.
  • Track context savings and compression behavior during long coding sessions.
  • Share compressed context across compatible local agent workflows.

Safety and Privacy

Compression trades context volume for abstraction. For debugging, security review, and incident work, retrieve and inspect originals before drawing final conclusions. Protect local storage because it may contain exactly the sensitive material that was removed from the active prompt.

Duplicate Check

Existing context-window optimizer entries cover agent behavior and workflow guidance. No chopratejas/headroom MCP entry or source URL was found in content/mcp.

#context#compression#tokens#memory#local-first

Source citations

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