"author:princeruhulofficial" matching MCP servers:

Audits MCP tool descriptions for quality and reliability, scoring them 0-100, detecting smells, and providing rewritten versions for better agent accuracy.

Resolves messy company, customer, and vendor names across CRM, ERP, and ITSM systems to canonical records using fuzzy matching, preventing AI agents from updating or creating wrong or duplicate records.

MCP server that scores tool descriptions, estimates token costs, simulates agent tool selection, and generates reliability reports to help AI agents choose the right tools and reduce wasted tokens.

MCP server for measuring, tracking, scoring, and improving AI agent reliability with tools for recording interactions, scoring reliability, analyzing failures, recommending improvements, generating audit reports, and checking MCP health.

MCP server that computes trust scores, permission decisions, and silent-failure risk for AI agents with tools for reliability scoring, silent failure detection, permission evaluation, and audit report generation.

This MCP server provides tools to manage, score, compress, and prune AI agent conversation context, helping keep agents focused and reduce token costs. It is a free, local, pure Python solution for any MCP client.

Protects AI agents from bad tool outputs, schema mismatches, and tool poisoning by validating, sanitizing, and scoring reliability of tool responses.

MCP server that detects and guards against tool poisoning and prompt injection attacks in tool descriptions and schemas. It provides risk scoring, pattern detection, safe rewriting, and audit reports with zero external API cost.

MCP server that helps AI agents analyze medical insurance claims, flag anomalies, score risk, and suggest next actions.

Reduces token costs from MCP tool schemas by analyzing bloat, compressing descriptions, and selecting only relevant tools for AI agents.

Analyzes multi-step AI agent tool chains to compute success probability, identify bottlenecks, and suggest better execution orders, enabling more reliable agents via local pure-math computation.

MCP server that analyzes AI agent execution logs to calculate reliability scores, detect failure patterns, and suggest concrete improvements for making AI agents more reliable.

Logs tool call results, calculates reliability scores, and generates reports for AI agents, helping entrepreneurs measure agent reliability without paid APIs.

Enables precise financial analysis of AI agent costs, including token pricing, multi-step run estimates, model comparison, and ROI versus human labor, with deterministic decimal math.

Provides accurate SaaS metrics calculations (LTV, CAC, runway, health score, etc.) with formulas and interpretations for AI agents and founders, ensuring no hallucinated numbers.

Scores AI agent trajectories and detects silent failures, loops, and reliability issues with zero external API cost.

Logs AI agent actions, computes reliability scores, generates audit reports, detects anomalies, and estimates risk to ensure agent accountability.

Computes AI agent reliability metrics like success rates, latency statistics, and failure patterns from provided numbers, with zero external API cost.

Calculates ROI, break-even, and cost projections for AI agents and automation projects, providing instant answers without external APIs.