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Documentation Index

Fetch the complete documentation index at: https://docs.universalbench.dev/llms.txt

Use this file to discover all available pages before exploring further.

Connect one MCP URL to your AI of choice and your agent instantly gains the ability to run Python and Bash, query and write databases, push code to GitHub with built in validation gates, call any LLM, search the web live, manage encrypted secrets, and more. Thirty three first class capabilities behind a single authenticated endpoint.

How it is different from every other MCP service

Most MCP services follow a standard pattern: paste an endpoint plus an API key in a header, hope your AI client supports headers properly, debug auth errors. UniversalBench is built differently on purpose.

Personal MCP URL

Sign up and your dashboard shows one URL with your identity baked in. Paste it. Done. No headers, no env vars, no config bugs. Works in every MCP client even the ones with poor header support.

96.5 percent fewer tokens

Real Anthropic API tests, not estimates. Log analysis tasks drop from 4024 tokens to 141. At scale this is $42,519 saved per year on a single workload.

Validation before deploy

safe_deploy runs five gates (lint, syntax, dry run, deps, rollback SHA) before code reaches GitHub. After push it hits your smoke test URL. If anything fails it reverts automatically.

Sessions and adaptive cache

session_id keeps Python state warm across calls. Repeated reads return instantly from a 30 second TTL cache. Standard MCPs have neither.

Get started

Quickstart

Get your personal URL and run your first call in under five minutes.

API reference

Every capability with descriptions, examples, and a live playground.

Advanced patterns

Sessions, adaptive caching, safe deploys, secrets vault.

Use it with any AI

Setup guides for Claude Desktop, Cursor, Claude Code, Windsurf, and any MCP client.

Proven results

These numbers come from real Anthropic API calls on real data. Reproducible in any account.
TaskWithout UniversalBenchWith UniversalBenchResult
Web search current dataFailed: “I cannot search the web”Succeeded with live sourcesCapability win
Math accuracy on 50 problems773 tokens, 2 wrong540 tokens, all correct30 percent fewer tokens, more accurate
Log analysis (1000 lines)4024 tokens, 37 errors found incorrectly141 tokens, 41 errors found correctly96.5 percent reduction
At 10,000 queries per day, the log analysis use case alone saves 42,519peryearagainstanEnterprisetierof42,519 per year against an Enterprise tier of 5,988. That is 610 percent ROI on a single workload.

What it works with

UniversalBench speaks MCP, so it works with any compatible AI client. Verified end to end with Claude (Anthropic), ChatGPT, Cursor, Claude Code, Windsurf, and custom agents built on the official MCP SDKs.