{
 "schema_version": 1,
 "generated": "2026-10-20",
 "id": "headroomlabs-ai-headroom--let-agents-retrieve-full-compressed-text",
 "repo": "headroomlabs-ai/headroom",
 "repo_url": "https://github.com/headroomlabs-ai/headroom",
 "repo_slug": "headroomlabs-ai-headroom",
 "title": "Give an MCP client on-demand access to full originals",
 "page_title": "How to give an MCP client on-demand access to full originals with Headroom",
 "summary": "Install the Headroom MCP server so the model can fetch uncompressed text when it needs it.",
 "who": "A developer using an MCP-capable client who sometimes needs the full text of a compressed log or file.",
 "problem": "Compressed content may hide a detail the model needs, and there is no simple way to recover the original.",
 "outcome": "The client gets headroom_compress, headroom_retrieve and headroom_stats tools and can pull originals back on request.",
 "steps": [
  "Install the CLI with `pip install \"headroom-ai[all]\"`",
  "Register the MCP server with `headroom mcp install`",
  "In your MCP client, call headroom_compress on a large payload",
  "Call headroom_retrieve when the model needs the full original text",
  "Check usage with headroom_stats"
 ],
 "agent_prompt": "Using the repo headroomlabs-ai/headroom, run `headroom mcp install` and connect the MCP client to the headroom server. Compress the file <file-path> with headroom_compress, then call headroom_retrieve to get the original back. Expected output: the original text matches the source file. Verify that the retrieved text equals the source.",
 "agents": [
  "any"
 ],
 "difficulty": "Advanced",
 "time": "45 minutes",
 "basis": "readme",
 "caveat": "The README says registry authors should use the canonical server.json rather than prose, so verify the exact MCP registration your client expects before relying on it.",
 "category": "Software and DevOps",
 "tags": [
  "Software and DevOps"
 ],
 "lang": "python",
 "requirements": [
  "Python 3.13 with uv, or pip, to install the headroom-ai package",
  "An account with an LLM provider such as Anthropic or OpenAI",
  "A coding agent or app that sends prompts through an API"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": false,
 "score": 0.7,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/headroomlabs-ai-headroom/let-agents-retrieve-full-compressed-text/",
 "md": "https://dedupedai.com/use-cases/headroomlabs-ai-headroom/let-agents-retrieve-full-compressed-text/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/headroomlabs-ai-headroom--let-agents-retrieve-full-compressed-text.json",
 "api_version": "v1"
}