"MCP servers to enhance agent-based medical project performance" matching MCP servers:

A Python-based MCP server that analyzes PostgreSQL query plans, suggests indexes, and checks pagination safety to help optimize database performance.

A comprehensive, AI-powered performance analysis and monitoring platform for OpenShift/Kubernetes clusters. This project provides Model Context Protocol (MCP) servers for analyzing etcd, network, and OVN-Kubernetes components with deep performance insights, automated root cause analysis, and actionable recommendations.

MCP servers for real-time development monitoring and interactive pair programming.

Provides tools for DeepTempo AI SOC including findings and case management, investigation workflow orchestration, action approval workflows, and MITRE ATT&CK layer generation.

A read-only MCP server that exposes local coding-agent session logs as three tools for introspection of recent work, debugging tool failures, and tracking token usage and estimated cost without parsing log files.

MCP server for AI agent observability, providing trace and span logging, search, latency/tokens/cost metrics, and anomaly detection using an in-memory buffer.

Tracks AI agent token usage and spending in real time, with budget alerts, per-task cost breakdown, and a visual dashboard.

Cryptographic accountability for AI agents. Ed25519-signed receipts for every MCP tool call. Constraints, chains, AI judgment, invoicing, and local dashboard included.

Enables agents to report and query performance metrics to build a community-driven quality database for MCP tools. This server helps agents discover and select the most reliable tools based on success rates and user-reported quality scores.

MCP server exposing AI agent observability tools: list, inspect, and deterministically replay recorded agent execution traces for debugging failures.

Collects page performance metrics using Playwright, CDP, and Lighthouse, and returns a normalized PerformanceReport.

Analyzes AI agent traces to diagnose failures, score performance, and provide actionable fixes using tools like judge_trace and trace_breakdown.

AI supply chain security scanner for MCP servers and AI agents. 18 tools for CVE scanning, blast radius mapping, CIS benchmarks, SBOM generation, and compliance enforcement across OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF, and EU AI Act.

Enables analyzing coding-agent session transcripts to measure tool efficiency, detect loops, track test outcomes, and report costs, with MCP tools for listing sessions, analyzing sessions, finding loops, and generating cost reports.

Exposes rate-limit and usage data for AI coding agent CLIs as MCP tools, enabling querying of usage snapshots and reset times.

A security checkpoint for AI agents that intercepts and polices tool calls with allow/block/ask verdicts, taint tracking, and audit logging.

Provides AI agents with a transparent proxy and journal for every tool action, including pre-state snapshots and approval gates for risky operations. Enables per-action undo, rewind to a point in time, and a kill switch that agents cannot override.

Provides structured MCP tools for local runtime observability and debugging of React Native/Expo apps on Android, enabling evidence-first diagnostics such as screenshots, UI trees, per-request network capture, performance metrics, and CDP-based console/heap/profiling exports.

An MCP server that provides append-only, tamper-evident local receipts for AI agent actions, capturing command executions, outputs, and handoff evidence.

A comprehensive project management and workflow tracking system that integrates with Claude Code via MCP, automatically capturing sessions, tools, agents, and project tasks into a centralized dashboard and database.