mcp-agent-reliability
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-agent-reliabilitySimulate which tool an agent would pick for 'send email to John'"
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-agent-reliability
Make your AI agents more reliable.
This MCP server acts like a reliability coach for your agents.
It helps you:
Score how clear your tool descriptions are (so the agent picks the right one)
Estimate how many tokens your tools will cost
Simulate which tool an agent would choose for a task
Generate simple test prompts
Get a full reliability report
Built for entrepreneurs and teams who are tired of agents calling the wrong tools and burning money.
Why this exists (simple story)
Imagine you give a 10-year-old child a big list of 30 toys and say “go play with the right one”.
If the labels are confusing, the child will pick the wrong toy.
AI agents are the same.
When you connect many MCP servers, the agent sees a long menu of tools.
If the descriptions are vague, it picks the wrong tool → wasted tokens → failed tasks.
This server is the “label checker” and “practice teacher” for that menu.
Related MCP server: clair
Quick start
# clone
git clone https://github.com/princeruhulofficial/mcp-agent-reliability.git
cd mcp-agent-reliability
# install
npm install
# build
npm run build
# run (stdio)
npm startAdd to Claude Desktop / Cursor / any MCP client
{
"mcpServers": {
"agent-reliability": {
"command": "node",
"args": ["/absolute/path/to/mcp-agent-reliability/dist/index.js"]
}
}
}Or with npx (after publish):
{
"mcpServers": {
"agent-reliability": {
"command": "npx",
"args": ["-y", "mcp-agent-reliability"]
}
}
}Tools
Tool | What it does |
| Gives a 0-100 score + reasons + suggestions for a tool description |
| Rough token count for a list of tools |
| Predicts which tool an agent would pick for a prompt |
| Creates 3 test prompts you can run against your agent |
| Full summary of scores + token estimates |
All tools are pure computation — no paid API keys required.
Example
Score a description:
Tool: score_tool_description
name: create_invoice
description: Create a new invoice for a customer. Requires customer_id and amount. Returns invoice_id.You get something like:
{
"score": 85,
"reasons": ["Good length...", "Mentions inputs or outputs..."],
"suggestions": [],
"interpretation": "Excellent — agent should pick this tool reliably"
}Tech
TypeScript
Official
@modelcontextprotocol/sdkStateless-friendly (works with 2026 MCP updates)
Zero external cost for core features
Roadmap
Optional LLM-backed scoring (when you want higher accuracy)
Hosted version with dashboard
Integration with progressive disclosure patterns
License
MIT
Made with ❤️ for the Prevalid community
Founder: Prince Ruhul
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
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