NodeBench MCP

Make AI agents catch the bugs they normally ship.

One command gives your agent structured research, risk assessment, 3-layer testing, quality gates, and a persistent knowledge base — so every fix is thorough and every insight compounds into future work.

claude mcp add nodebench -- npx -y nodebench-mcp

Why — What Bare Agents Miss

We benchmarked 9 real production prompts — things like "The LinkedIn posting pipeline is creating duplicate posts" and "The agent loop hits budget but still gets new events" — comparing a bare agent vs one with NodeBench MCP.

The bare agent reads the code, implements a fix, runs tests once, and ships. The MCP agent researches first, assesses risk, tracks issues to resolution, runs 3-layer tests, creates regression guards, enforces quality gates, and banks everything as knowledge for next time.

Every additional tool call produces a concrete artifact — an issue found, a risk assessed, a regression guarded — that compounds across future tasks.

Who's Using It

Vision engineer — Built agentic vision analysis using GPT 5.2 with Set-of-Mark (SoM) for boundary boxing, similar to Google Gemini 3 Flash's agentic code execution approach. Uses NodeBench's verification pipeline to validate detection accuracy across screenshot variants before shipping model changes.

QA engineer — Transitioned a manual QA workflow website into an AI agent-driven app for a pet care messaging platform. Uses NodeBench's quality gates, verification cycles, and eval runs to ensure the AI agent handles edge cases that manual QA caught but bare AI agents miss.

Both found different subsets of the 75 tools useful — which is why v2.1 ships with --preset gating to load only what you need.

How It Works — 3 Real Examples

Example 1: Bug fix

You type: "The content queue has 40 items stuck in 'judging' status for 6 hours"

Bare agent: Reads the queue code, finds a potential fix, runs tests, ships.

With NodeBench MCP: The agent runs structured recon and discovers 3 blind spots the bare agent misses:

• No retry backoff on OpenRouter rate limits (HIGH)
• JSON regex match(/\{[\s\S]*\}/) grabs last } — breaks on multi-object responses (MEDIUM)
• No timeout on LLM call — hung request blocks entire cron for 15+ min (not detected by unit tests)

All 3 are logged as gaps, resolved, regression-tested, and the patterns banked so the next similar bug is fixed faster.

Example 2: Parallel agents overwriting each other

You type: "I launched 3 Claude Code subagents but they keep overwriting each other's changes"

Without NodeBench: Both agents see the same bug and both implement a fix. The third agent re-investigates what agent 1 already solved. Agent 2 hits context limit mid-fix and loses work.

With NodeBench MCP: Each subagent calls claim_agent_task to lock its work. Roles are assigned so they don't overlap. Context budget is tracked. Progress notes ensure handoff without starting from scratch.

Example 3: Knowledge compounding

Tasks 1-3 start with zero prior knowledge. By task 9, the agent finds 2+ relevant prior findings before writing a single line of code. Bare agents start from zero every time.

Quick Start

Install (30 seconds)

# Claude Code CLI — all 75 tools
claude mcp add nodebench -- npx -y nodebench-mcp

# Or start lean — 30 tools, ~60% less token overhead
claude mcp add nodebench -- npx -y nodebench-mcp --preset lite

Or add to ~/.claude/settings.json or .claude.json:

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

First prompts to try

# See what's available
> Use getMethodology("overview") to see all workflows

# Before your next task — search for prior knowledge
> Use search_all_knowledge("what I'm about to work on")

# Run the full verification pipeline on a change
> Use getMethodology("mandatory_flywheel") and follow the 6 steps

Optional: API keys for web search and vision

export GEMINI_API_KEY="your-key"        # Web search + vision (recommended)
export GITHUB_TOKEN="your-token"        # GitHub (higher rate limits)

What You Get

Core workflow (use these every session)

When running parallel agents (Claude Code subagents, worktrees)

Research and discovery

The Methodology Pipeline

NodeBench MCP isn't just a bag of tools — it's a pipeline. Each step feeds the next:

Research → Risk → Implement → Test (3 layers) → Eval → Gate → Learn → Ship
    ↑                                                              │
    └──────────── knowledge compounds ─────────────────────────────┘

Inner loop (per change): 6-phase verification ensures correctness. Outer loop (over time): Eval-driven development ensures improvement. Together: The AI Flywheel — every verification produces eval artifacts, every regression triggers verification.

Ask the agent: Use getMethodology("overview") to see all 18 methodology topics.

Parallel Agents with Claude Code

Based on Anthropic's "Building a C Compiler with Parallel Claudes" (Feb 2026).

When to use: Only when running 2+ agent sessions. Single-agent workflows use the standard pipeline above.

How it works with Claude Code's Task tool:

1. COORDINATOR (your main session) breaks work into independent tasks
2. Each Task tool call spawns a subagent with instructions to:
   • claim_agent_task — lock the task
   • assign_agent_role — specialize (implementer, test_writer, critic, etc.)
   • Do the work
   • release_agent_task — handoff with progress note
3. Coordinator calls get_parallel_status to monitor all subagents
4. Coordinator runs run_quality_gate on the aggregate result

MCP Prompts available:

• claude-code-parallel — Step-by-step Claude Code subagent coordination
• parallel-agent-team — Full team setup with role assignment
• oracle-test-harness — Validate outputs against known-good reference
• bootstrap-parallel-agents — Scaffold parallel infra for any repo

Toolset Gating (v2.1)

75 tools means ~19K tokens of schema per API call. If you only need core methodology, gate the toolset:

Presets

# Lite — 30 tools (verification, eval, gates, learning, recon)
claude mcp add nodebench -- npx -y nodebench-mcp --preset lite

# Core — 50 tools (adds flywheel, bootstrap, self-eval)
claude mcp add nodebench -- npx -y nodebench-mcp --preset core

# Full — all 75 tools (default)
claude mcp add nodebench -- npx -y nodebench-mcp

Or in config:

{
  "mcpServers": {
    "nodebench": {
      "command": "npx",
      "args": ["-y", "nodebench-mcp", "--preset", "core"]
    }
  }
}

Fine-grained control

# Include only specific toolsets
npx nodebench-mcp --toolsets verification,eval,recon

# Exclude heavy optional-dep toolsets
npx nodebench-mcp --exclude vision,ui_capture,parallel

# See all toolsets and presets
npx nodebench-mcp --help

Available toolsets

findTools and getMethodology are always available regardless of gating — agents can discover tools on demand.

Build from Source

git clone https://github.com/nodebench/nodebench-ai.git
cd nodebench-ai/packages/mcp-local
npm install && npm run build

Then use absolute path:

{
  "mcpServers": {
    "nodebench": {
      "command": "node",
      "args": ["/path/to/packages/mcp-local/dist/index.js"]
    }
  }
}

Troubleshooting

"No search provider available" — Set GEMINI_API_KEY, OPENAI_API_KEY, or PERPLEXITY_API_KEY

"GitHub API error 403" — Set GITHUB_TOKEN for higher rate limits

"Cannot find module" — Run npm run build in the mcp-local directory

MCP not connecting — Check path is absolute, run claude --mcp-debug, ensure Node.js >= 18

License

MIT