"Understanding Inference Models" matching MCP servers:

MCP server for achieving peak inference performance by detecting drift between code and runtime behavior, comparing against benchmarks, and providing optimization templates.

Enables governance-grade AIops for GPU inference clusters with root-cause analysis, metrics, and policy-governed operations for vLLM and Ray.

Flask Extension for apcore (AI-Perceivable Core) integration. Expose your Flask routes as MCP tools with auto-discovery, Pydantic schema inference, and built-in observability.

Enables classification of GPU workloads as training, inference, or idle from telemetry data, with tools for one-shot classification, benchmarking, and audit log verification.

8 standalone MCP servers for SMBs — Guardrails (prompt injection, PII, secrets detection), FinOps (AI cost tracking and budgets), Observability (agent tracing), Smart Router (17+ models, 9 providers), Trust Score (governance scoring 0-100), Memory (persistent agent memory), ThinkSecure (runtime security + SOC2/GDPR audit), and A2A Lite (agent-to-agent protocol). 37 tools total, TNC credits

An MCP server that connects language models to a Wazuh SIEM cluster, enabling read-only plain-language queries, schema inspection, field coverage analysis, and decoder testing.

Enables AI clients to query AxonHub admin data through natural language, including instance status, projects, channels, models, requests, and usage statistics, with read-only safeguards and sensitive-field redaction.

Model Context Protocol (MCP) Server for Vunet vuSmartMaps - A multi-tenant observability platform integration. Query metrics, traces, logs, and data models from your Vunet tenants using natural language through AI assistants like Claude, GitHub Copilot, or any MCP-compatible client.

Enables language models to access LangSmith observability platform features including fetching conversation history, managing prompts, retrieving traces and runs, working with datasets and examples, and analyzing experiments.

An MCP server that enables LLMs to retrieve structured network information, including routing, interfaces, MPLS, and topology, from devices using gNMI and OpenConfig models. It facilitates real-time network analysis, log filtering, and status monitoring through a standardized interface.

This project provides a Model Context Protocol (MCP) server that allows language models and other MCP clients to interact with a SuzieQ network observability instance via its REST API.

Enables MCP clients to interact with a local AI agent through Ollama, providing tools for checking weather, current time, and available models, while tracing calls to Langfuse.

An MCP server implementation that integrates AI assistants with Langfuse workspaces, allowing models to query LLM metrics by time range.

A specialized diagnostic framework that enables AI models to self-diagnose and fix MCP-related issues by analyzing logs, validating configurations, testing connections, and implementing solutions.

A Model Context Protocol (MCP) server for the Pingera monitoring service, providing seamless integration between AI models and monitoring data.

Provides AI assistants with direct access to Red Hat OpenShift AI observability data, enabling querying of Prometheus metrics, Alertmanager alerts, Loki logs, Grafana dashboards, and Kubernetes cluster state to troubleshoot vLLM inference workloads.

An open-source MCP server that collects errors from browser console and terminal, then uses OpenRouter's free AI models to generate summaries for AI agents.

Enables real-time AI behavioral drift monitoring and detection for Claude Code, tracking shifts in model behavior across 11 major models with tools for scorecards, version changes, and compliance reports.

Enables AI agents to reach upstream MCP tool servers through a central policy gateway that authenticates each agent, enforces least-privilege and default-deny rules, and applies response obligations such as redaction, inference stripping, PII masking, and truncation. It also records every decision in a tamper-evident, hash-chained audit log that can be verified later.

Enables local-first tracking of AI token usage and costs from Claude Code and OpenCode, answering queries about tokens, models, sessions, and cost through MCP tools and a CLI, with no network calls.