How I Cut 45,000 Next.js Error Tokens to 118 in <0.2ms Using a Rust MCP Server

Vidyax — Tue, 15 Sep 2026 03:19:09 +0000

If you use Cursor, Claude Desktop, or Cline daily,
you have probably watched your rate limits evaporate
because of a single runtime crash.

A Next.js build fails or a Python script panics, and

your terminal vomits 500 lines of stack traces. The
LLM eagerly ingests all 45,000 tokens of internal
node_modules machinery, webpack bundles, and event
loop frames.

You just paid $0.15 for an AI model to read code it

can't edit, your prompt cache is wiped, and your agent
is now hallucinating because its context window is
full of garbage.

Worse, your terminal stderr probably just leaked

DATABASE_URL=postgres://admin:password@... straight
to an external API.

I got tired of paying for framework noise, so I

built an open-source Rust Model Context Protocol (MCP)
server called Tokenectomy Razor to fix it locally
before logs ever touch the LLM.

## What is eating your context window?

A typical Next.js error trace looks like this:


text
    TypeError: Cannot read properties of undefined
  (reading 'digest')
        at Object.<anon> (/node_modules/next/bundle5.
  js:142:31)
        at __webpack_require__
  (/node_modules/next/bundle5.js:198:12)
        at Object.execute (/node_modules/next/dev-
  server.js:412:19)
        at processTicksAndRejections (task_queues:95:5)
        Database connection failed:
  postgresql://admin:super_secret_password@db.prod.
  internal:5432/primary
        API key leaked: sk-ant-api03-
  abcdef1234567890abcdef1234567890
        [... 480 internal dependency frames flooding
  context ...]

  Notice three things:

  1. 98% of those frames are inside node_modules. Your
  AI agent is not going to edit webpack's internal
  bundle logic. It only cares about the one line in
  src/components/Header.tsx:42 where you missed a
  parenthesis.
  2. The database password and API key are sitting
  unmasked in plain text.
  3. The raw token count for this single error dump was
  45,820 tokens.

  ## What happens after Tokenectomy runs

  When the AI agent invokes get_error_context via MCP,
  Tokenectomy intercepts the log, strips framework
  internals, redacts all secrets locally using a
  deterministic DFA regex, and grabs bounded source code
  lines around the actual crash:

    [:TOKENECTOMY:M2M_CONTROL_PLANE:v1.3.0]
    [STATE=FRAMEWORK_NOISE_PURGED]
    [STRATEGY_APPLIED=AGGRESSIVE]
    [ORIGINAL_BYTES=45820 | CLEAN_BYTES=118 |
  REDUCTION=99%]
    [PRIMARY_CRASH_COORDINATES=src/components/Header.
  tsx:42]

  [COGNITIVE_DIRECTIVE=INSPECT_CALLER_AT_src/components/
  Header.tsx:42]
    [:END_CONTROL_PLANE]

    src/components/Header.tsx:42:15 - SyntaxError
      42 |   const user = useSession( ;
         |                           ^ Expected ')'
    🛡️ [CONNECTION_STRING_REDACTED]
    🛡️ [REDACTED]
  ANTHROPIC_API_KEY=[REDACTED_SECRET_KEY]

  Final token count: 118 tokens.
  Reduction: 99.7%.
  Zero credentials leaked to the cloud.

  ## Why Rust and why local-first?

  I did not want another slow node script or cloud proxy
  adding 300ms of network latency to an agent loop.

  1. Sub-millisecond latency: The core log surgery and
  secret redaction pipeline runs in <0.2 milliseconds on
  an Intel i5 CPU.
  2. Zero cloud leaks: It runs 100% locally over stdio.
  Your error logs, environment variables, and
  proprietary code never touch an external server.
  3. AST verification & rollback: The apply_code_patch
  tool parses modified code with Tree-sitter before
  saving to disk. If the agent generates invalid syntax,
  it immediately rolls back with zero dirty git diff.
  4. Lightweight footprint: Baseline process memory is
  3.45 MB VmRSS.

  Glama.ai audited the server definition under their
  Tool Definition Quality Score (TDQS) and awarded it
  Grade A (4.7 / 5.0) across all tools.

  ## How to set it up (Takes 30 seconds)

  You don't need to install Rust or compile anything. We
  distribute pre-built native binaries via npm for
  Linux, macOS (Apple Silicon & Intel), and Windows.

  Add this to your claude_desktop_config.json or Cursor
  MCP settings:

    {
      "mcpServers": {
        "tokenectomy": {
          "command": "npx",
          "args": ["-y", "tokenectomy-razor", "--mcp"]
        }
      }
    }

  Or if you prefer native cargo:

    cargo install tokenectomy

  Then configure:

    {
      "mcpServers": {
        "tokenectomy": {
          "command": "razor",
          "args": ["--mcp"]
        }
      }
    }

  ## Open Source & Repositories

  Everything is open source under the MIT license:

  1. GitHub: https://github.com/Tokenectomy-
  Labs/Tokenectomy
  2. Glama: https://glama.ai/mcp/servers/Tokenectomy-
  Labs/Tokenectomy
  3. npm: https://www.npmjs.com/package/tokenectomy-
  razor
  4. crates.io: https://crates.io/crates/tokenectomy

  Give it a run next time your agent is about to eat a
  40,000-token crash log. Your token bill will thank
  you.

DEV Community: Vidyax

How I Cut 45,000 Next.js Error Tokens to 118 in <0.2ms Using a Rust MCP Server