Mcp Audit
Skill Verified ActiveAudit connected MCP servers for token overhead, redundancy, and security. Use when sessions feel slow or before adding new MCPs.
To help users manage their MCP server configurations effectively by identifying and rectifying token overhead and redundancy, leading to faster sessions and more efficient resource utilization.
Features
- List active MCP servers and their configurations
- Estimate token overhead per MCP server
- Identify redundant or unused MCP servers
- Provide actionable recommendations for disabling or filtering servers
- Calculate projected token savings
Use Cases
- When Claude sessions feel slow or expensive
- Before adding new MCP servers to an existing configuration
- When the LLM context window fills up quickly
- For regular reviews of project configuration and MCP server utilization
Non-Goals
- Modifying MCP server configurations directly
- Managing MCP server authentication or credentials
- Auditing the functionality or correctness of individual MCP tools
Installation
First, add the marketplace
/plugin marketplace add rohitg00/pro-workflow/plugin install pro-workflow@pro-workflowQuality Score
VerifiedTrust Signals
Similar Extensions
Setup
100Use first for install/update routing — sends setup, doctor, or MCP requests to the correct OMC setup flow
Mcp Setup
100Configure popular MCP servers for enhanced agent capabilities
Mongodb Mcp Setup
100Guide users through configuring key MongoDB MCP server options. Use this skill when a user has the MongoDB MCP server installed but hasn't configured the required environment variables, or when they ask about connecting to MongoDB/Atlas and don't have the credentials set up.
MongoDB Connection Optimizer
100Optimize MongoDB client connection configuration (pools, timeouts, patterns) for any supported driver language. Use this skill when working/updating/reviewing on functions that instantiate or configure a MongoDB client (eg, when calling `connect()`), configuring connection pools, troubleshooting connection errors (ECONNREFUSED, timeouts, pool exhaustion), optimizing performance issues related to connections. This includes scenarios like building serverless functions with MongoDB, creating API endpoints that use MongoDB, optimizing high-traffic MongoDB applications, creating long-running tasks and concurrency, or debugging connection-related failures.
Rule Effectiveness Analysis
100Analyze which rules are actively used vs inert. Detect coverage gaps. Recommend pruning to reduce token consumption.
TradeMemory Protocol
100Domain knowledge for the Evolution Engine — LLM-powered autonomous strategy discovery from raw OHLCV data. Covers the generate-backtest-select-evolve loop, vectorized backtesting, out-of-sample validation, and strategy graduation. Use when discovering trading patterns, running backtests, evolving strategies, or reviewing evolution logs. Triggers on "evolve", "discover patterns", "backtest", "evolution", "strategy generation", "candidate strategy".