Tamperlens MCP Server

tamperlens-mcp

MCP server for Tamperlens — ask whether a document was edited, whether its redactions held, and whether it is safe for a model to read, before it reaches your agent's context.

// Claude Desktop / Claude Code — claude_desktop_config.json or .mcp.json
{
  "mcpServers": {
    "tamperlens": {
      "command": "npx",
      "args": ["-y", "tamperlens-mcp"],
      "env": { "TAMPERLENS_API_KEY": "tl_..." }
    }
  }
}

No key is required to try it — without one you get the anonymous allowance of 10 documents an hour. A free key at tamperlens.com/account raises that to 50 a month with no card.

Tools

Accepts PDF; Word, Excel and PowerPoint (.docx, .xlsx, .pptx and the macro-enabled twins); and JPEG, PNG, WebP, HEIC/HEIF and AVIF.

A local path or a URL

inspect_document, check_redaction and compare_documents (as originalUrl/candidateUrl) take either a local path or an http/https url — not both. A url is fetched by this server, on your own machine, over your own network, and only the bytes are sent on to Tamperlens. That is why the option lives here and not on the REST API: a server-side fetch of a caller-supplied URL is an SSRF risk on the API host, whereas fetching from your machine reaches only what you can already reach. It is still guarded: http/https only (re-checked at every redirect), a redirect cap, a 30s timeout (TAMPERLENS_FETCH_TIMEOUT_MS to tune), and the same 10 MB cap, enforced on the bytes received rather than a Content-Length that can lie.

It also checks the destination IP, not just the scheme: a url that points at — or redirects to, or resolves by DNS to — a loopback, link-local/metadata (169.254.169.254), private or otherwise non-routable address is refused, and the address it connects to is pinned to the one it validated, so DNS rebinding cannot slip an internal address past the check. The check re-runs at every redirect hop. If you genuinely need to fetch from an internal host, allow-list specific addresses with TAMPERLENS_FETCH_ALLOW_IPS (comma-separated; empty by default, and the default reaches nothing internal).

The path argument reads any local file and sends its bytes to the API. inspect_document, triage_document, check_redaction and compare_documents read whatever local path they are given — there is no directory sandbox and no extension filter, because reading a document off disk is the whole job and an intake path is not knowable in advance. The bytes of that file are then transmitted to the configured Tamperlens deployment. A model an attacker is steering could therefore name a private file (~/.ssh/id_rsa, ~/.aws/credentials) and cause its bytes to leave the machine. Run this server only with agents and inputs you trust, the same rule you would apply to any tool that can read the local filesystem. For a hard boundary, front the intake with a directory you control and pass only paths inside it.

Why you would put this in front of an agent

A document is read by two audiences and only one of them sees the page. An extraction pipeline reads the title, the keywords, the XMP packet, the comments, the names of attachments — fields that exist to be read by software. Text placed there can be written to be obeyed rather than read.

inspect_document reports that before your agent ingests the file, and the recovered payload is elided unconditionally in this server. That is not a convenience: the most likely next reader of a Tamperlens report is the same model that was about to read the document, so an MCP server that echoed the attacker's sentence back into the context would be completing the attack it just detected. You get the field, the location and the cue categories. You do not get the sentence.

What it does not do

• No local analysis. Every tool is a thin wrapper over the Tamperlens REST API on a running deployment. There is no second engine here to drift from the first.
• No verdicts. Tamperlens reports risk signals with the evidence behind them, never "this document is fraudulent". Combine them with your own decision logic.
• The document never enters the model's context. Tools take a path on the caller's filesystem (or a URL fetched here) and read the bytes on this side. Base64 in a tool call would put megabytes of document into the context window to reach a service that needs the bytes, not the model.
• Nothing is stored. Files are parsed in memory by the API and discarded with the response.

Configuration

Links

Field guide to every signal · Is this file safe for your model to read? · API reference · Security and privacy posture

MIT licensed. Issues and source: github.com/haruodev/tamperlens-mcp.