# mcp-tool-chain-optimizer

**MCP server that makes multi-step AI agent tool chains more reliable.**

Analyze any sequence of tools → get success probability, find the bottleneck, see a better execution order, and receive concrete improvement tips.  
Everything runs **locally with pure math** – zero external API calls, zero extra cost.

Built for entrepreneurs and AI builders who want accountable, predictable agents (part of the Prevalid AI Execution OS vision).

## Why this exists

When an AI agent chains 5–10 tools together, small failure rates multiply:

- 90% × 85% × 92% × 80% ≈ **56%** overall success  
- One weak “critical” tool can silently kill the whole workflow  
- Cost and latency explode without anyone noticing

This MCP server gives the agent (or the human developer) a fast, free way to measure and improve that chain **before** it goes to production.

## Tools

| Tool | What it does |
|------|--------------|
| `analyze_tool_chain` | Full report: probability, risk level, cost, latency, bottleneck, suggestions, better order |
| `estimate_chain_success` | Quick probability from a simple list of success rates |
| `find_bottlenecks` | Rank the weakest links (success rate × impact) |
| `suggest_better_order` | Fail-fast reordering that still respects dependencies |
| `generate_reliability_report` | Human-readable Markdown report ready to share with stakeholders |

## Quick Start

```bash
# Install
pip install -e .

# Run the MCP server (stdio)
mcp-tool-chain-optimizer
# or
python -m mcp_tool_chain_optimizer.server
```

### Claude Desktop / Cursor / any MCP client

Add to your MCP config:

```json
{
  "mcpServers": {
    "tool-chain-optimizer": {
      "command": "python",
      "args": ["-m", "mcp_tool_chain_optimizer.server"],
      "cwd": "/path/to/mcp-tool-chain-optimizer"
    }
  }
}
```

## Example

```json
[
  {"name": "web_search", "success_rate": 0.92, "avg_latency_ms": 800, "cost_per_call": 0.002, "failure_impact": "medium"},
  {"name": "extract_entities", "success_rate": 0.78, "avg_latency_ms": 300, "cost_per_call": 0.001, "failure_impact": "high"},
  {"name": "write_summary", "success_rate": 0.95, "avg_latency_ms": 1200, "cost_per_call": 0.005, "failure_impact": "low", "depends_on": ["extract_entities"]}
]
```

→ Overall success ≈ 68%, bottleneck = `extract_entities`, suggested order puts the risky extractor earlier (fail-fast).

## Design Principles

- **Type A (mcpize)**: pure computation, zero paid API
- Local-first, privacy-friendly
- Fast enough for real-time agent self-reflection
- Simple JSON in / Markdown out – works with any LLM

## Development

```bash
pip install -e ".[dev]"
pytest
```

## License

MIT

---

Made with ❤️ for the Prevalid community – making AI agents accountable at the infrastructure level.