"Créer des systèmes de prompt pour les modèles de langage (LLM)" matching MCP servers:

Connects AI assistants to turbometrics.de for website performance analysis, scans, real user monitoring, and alerts management.

Enables agents to check remaining Anthropic and OpenAI rate-limit quotas with reset times, plus Hermes Agent token usage, so bots can throttle or stop before hitting limits.

Measured latency and uptime for 45 hosted AI inference APIs: independent probes from 4 regions every 5 minutes, no gateway. Exposes get_ai_api_latency (TTFB p50/p95 and uptime rankings by region). Data CC BY 4.0.

LLM cost optimization and provider usage visibility for MCP-capable agents.

Enables benchmarking of Large Language Model APIs by measuring performance metrics such as generation throughput, prompt throughput, and Time To First Token (TTFT) with configurable concurrency levels and parameters.

Enables inference engines to match longest common prompt token prefixes via a radix trie, so cached KV-cache blocks are reused instead of re-prefilled. This eliminates redundant prefill computation and lowers time-to-first-token, alongside paged attention allocation, speculative decoding verification, INT8 quantization, and latency telemetry.

Extracts structured incident analysis from raw communication logs, classifying events by NIST SP 800-61 phases with severity assessment and responder metrics.

MCP server for Tessera's LLM cost-optimization layer. Anchored spend, drift detection, recommendations as typed tools.

An MCP server that functions as an intelligent gateway for multiple LLM backends including OpenAI, Claude, and Ollama. It supports automatic provider fallback, streaming responses via Server-Sent Events, and real-time monitoring for robust AI integration.

Um servidor MCP completo para integração com Sentry no Cursor, oferecendo 27 ferramentas para monitoramento de erros, performance e saúde de aplicações.

Enables log analysis for Tenable Patch Management (SaaS and on-prem) by parsing, de-duplicating, and decoding server/client logs into structured verdicts, ranked issues, and known-issue fixes.

Unified MCP safety server that detects prompt injection (75 patterns), scans LLM outputs for leaked secrets/PII, enforces API cost budgets, and creates signed audit trails. Zero ML dependencies, pure Python.

Real-time cluster health monitoring, pre-request NLMS latency prediction, and intelligent prompt routing across multi-instance LLM backends (vLLM, Ollama, SGLang, TGI).

A Python utility for adding usage tracking, analytics, and audit trails to MCP servers using SQLite-backed persistence. It enables developers to monitor tool, prompt, and resource activity and expose these statistics directly to LLM clients.

Provides read-only access to the Cloudera CDP Public Cloud control plane, allowing users to list and describe environments, datahubs, datalakes, DW clusters, virtual warehouses, DE services, ML workspaces, and IAM users via natural language. It uses the installed cdp CLI and supports profiles.

Enables security analysis of Kubernetes Role configurations using LLM-assisted prompt chaining and rule-based assessment. Provides comprehensive security scoring, hardened role generation, and runtime permission usage correlation to identify privilege escalation risks and over-permissive configurations.

MCP-native agent evaluation and observability server. Log traces, evaluate output quality with 12 built-in rules (PII detection, prompt injection, cost thresholds), and track agent costs. Real-time dashboard, OTel-compatible spans. Self-hosted, MIT licensed.

A Model Context Protocol server that provides LLM Agents with a comprehensive toolset for IP geolocation, network diagnostics, system monitoring, cryptographic operations, and QR code generation.

Relays error logs from other MCP servers when an LLM call fails and the returned error message is unclear, helping the model handle errors more intelligently.

MCP server that diagnoses Apache Spark job failures and optimizes performance using stack-trace analysis and LLM providers, supporting EMR and local sources.