mcp-tool-chain-optimizer
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@mcp-tool-chain-optimizerAnalyze my tool chain: web_search 92%, extract_entities 78%, write_summary 95%"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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.
Related MCP server: ucon-mcp
Tools
Tool | What it does |
| Full report: probability, risk level, cost, latency, bottleneck, suggestions, better order |
| Quick probability from a simple list of success rates |
| Rank the weakest links (success rate × impact) |
| Fail-fast reordering that still respects dependencies |
| Human-readable Markdown report ready to share with stakeholders |
Quick Start
# Install
pip install -e .
# Run the MCP server (stdio)
mcp-tool-chain-optimizer
# or
python -m mcp_tool_chain_optimizer.serverClaude Desktop / Cursor / any MCP client
Add to your MCP config:
{
"mcpServers": {
"tool-chain-optimizer": {
"command": "python",
"args": ["-m", "mcp_tool_chain_optimizer.server"],
"cwd": "/path/to/mcp-tool-chain-optimizer"
}
}
}Example
[
{"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
pip install -e ".[dev]"
pytestLicense
MIT
Made with ❤️ for the Prevalid community – making AI agents accountable at the infrastructure level.
Related MCP Servers
- Flicense-qualityDmaintenanceAI agents hallucinate math. Euclid fixes that. Connect once and give your agent 10 deterministic tools: arithmetic, unit conversion, statistics, datetime, finance, regex, color, encoding, validation, and geospatial. Same input, same output, every time.Last updated2
- Alicense-qualityAmaintenanceVerified unit conversion and dimensional analysis for AI agents. 190+ units, 31 domain formulas (clinical, physics, aerospace, SRE), physical constants with uncertainty propagation. Refuses invalid conversions structurally: the tool that won't convert mg to mL and knows the difference between torque and energy.Last updatedAGPL 3.0
- AlicenseBqualityAmaintenanceEnables AI agents to efficiently solve problems by estimating complexity, pruning unnecessary paths, and focusing search through web search, code analysis, and persistent investigation tracking.Last updated74MIT
- Alicense-qualityCmaintenanceEnables LLM agents to perform SRE reliability calculations like error budgets and burn rates using deterministic tools, integrating with Prometheus and Loki for real data.Last updatedMIT
Related MCP Connectors
Agent-to-agent reasoning-as-a-service: chain-of-thought, analysis, and decision support.
Multi-Agent AI Validation: X-Z-CS Trinity. 13 tools FREE. Auditable reasoning. v0.5.54
Precision math engine for AI agents. 203 exact methods. Zero hallucination.
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/princeruhulofficial/mcp-tool-chain-optimizer'
If you have feedback or need assistance with the MCP directory API, please join our Discord server