@computegauge/mcp

The cost intelligence + agent credibility layer for AI agents. Install once, every session is cost-aware and credibility-building.

npm install -g @computegauge/mcp

---

Why This Exists

AI agents are powerful but expensive. A single Claude Code session can cost $5-50+. A multi-agent workflow can burn through $100 in minutes. And nobody tells the agent — or the human — until the bill arrives.

ComputeGauge MCP makes agents cost-aware AND gives them a credibility score. Any MCP-compatible agent (Claude, Cursor, Windsurf, custom agents) gets:

1. Cost Intelligence — Know which model is optimal for every task, track spend in real-time
2. Agent Credibility — Build a reputation score (0-1000) by making smart decisions. Compete on a leaderboard.
3. Local→Cloud Routing — Detect when local inference isn’t good enough, route to cloud, earn credibility for smart routing

The result: agents that spend 40-70% less, build visible credibility, and know when to route to cloud.

---

Setup — 30 Seconds

Claude Desktop / Claude Code

Add to ~/.config/claude/claude_desktop_config.json:

{
  "mcpServers": {
    "computegauge": {
      "command": "npx",
      "args": ["-y", "@computegauge/mcp"]
    }
  }
}

That’s it. Restart Claude. Every conversation now has cost intelligence + credibility tracking.

With Provider API Keys (Enhanced)

{
  "mcpServers": {
    "computegauge": {
      "command": "npx",
      "args": ["-y", "@computegauge/mcp"],
      "env": {
        "ANTHROPIC_API_KEY": "sk-ant-...",
        "OPENAI_API_KEY": "sk-...",
        "COMPUTEGAUGE_BUDGET_TOTAL": "50"
      }
    }
  }
}

With Local Inference (Ollama, vLLM, etc.)

{
  "mcpServers": {
    "computegauge": {
      "command": "npx",
      "args": ["-y", "@computegauge/mcp"],
      "env": {
        "OLLAMA_HOST": "http://localhost:11434",
        "OLLAMA_MODELS": "llama3.3:70b,qwen2.5:7b,deepseek-r1:14b",
        "ANTHROPIC_API_KEY": "sk-ant-...",
        "COMPUTEGAUGE_BUDGET_TOTAL": "50"
      }
    }
  }
}

Cursor

Add to Cursor MCP settings:

{
  "computegauge": {
    "command": "npx",
    "args": ["-y", "@computegauge/mcp"]
  }
}

---

Tools Reference

Agent-Native Tools (use automatically every session)

Credibility Tools (the reputation protocol)

Intelligence Tools (for user questions)

Resources

Prompts

---

Agent Credibility System

Every smart decision earns credibility points on a 0-1000 scale:

Credibility Tiers

Earnable Badges

---

Local Cluster Integration

ComputeGauge auto-detects local inference endpoints:

Set OLLAMA_MODELS="llama3.3:70b,qwen2.5:7b" (comma-separated) to declare available models.

The Local→Cloud Routing Flow

1. Agent calls assess_routing("code_generation", quality="good")
2. ComputeGauge checks: local llama3.3:70b quality for code_generation = 80/100
3. "Good" quality threshold = 78 → Local model is sufficient!
4. Agent uses local model → saves money → earns credibility for honest assessment

OR:

1. Agent calls assess_routing("complex_reasoning", quality="excellent")
2. ComputeGauge checks: local llama3.3:70b quality for complex_reasoning = 78/100
3. "Excellent" quality threshold = 88 → Quality gap of 10 points → Route to cloud!
4. Agent calls pick_model → gets Claude Sonnet 4 → executes → calls route_to_cloud
5. Agent earns +70 credibility points for smart routing decision

---

How pick_model Works

The decision engine scores every model across three dimensions:

Quality — Per-task-type scores for 14 task types Cost — Real pricing from 8 providers, 20+ models, calculated per-call (log-scale normalization) Speed — Relative inference speed scores

---

Model Coverage

Local Models Supported

---

Environment Variables

---

For Agent Developers

If you’re building AI agents (via Claude Agent SDK, LangChain, CrewAI, AutoGen, etc.), ComputeGauge MCP is the easiest way to add cost awareness AND agent credibility:

1. Zero integration effort — Just add the MCP server to your agent’s config
2. No code changes — The agent discovers 18 tools via MCP protocol automatically
3. Immediate value — pick_model returns recommendations on first call, credibility tracking starts automatically
4. Session tracking built-in — Full cost visibility per agent run
5. Credibility system — Your agent earns a visible reputation score that users can see
6. Local cluster support — Auto-detect and leverage on-prem inference
7. Budget guardrails — Warnings when approaching limits

Pattern: Cost-Aware + Credibility-Building Agent Loop

1. Agent receives task
2. Agent calls assess_routing(task_type) → local or cloud?
3. Agent calls pick_model(task_type, priority="balanced")
4. Agent uses recommended model for the task
5. Agent calls log_request(provider, model, tokens)
6. Agent calls rate_recommendation(model, rating, success)
7. If cloud-routed: agent calls route_to_cloud(task_type, reason, model)
8. Every 5 requests, agent calls session_cost()
9. If session cost > 80% of budget, switch to priority="cheapest"
10. At session end: check credibility_profile()

This pattern reduces costs by 40-70% while building a credibility score that makes users trust the agent more.

---

License

Apache-2.0 — Free to use, modify, and distribute.

Links

• ComputeGauge Dashboard
• CLI (@computegauge/cli)
• Adapters (@computegauge/adapters)