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

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    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.
    Last updated
    4
    MIT
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    quality
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    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.
    Last updated
    9
    74
    7
    MIT
  • A
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    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
    Last updated
    4
    1
    MIT
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    Enables LLM agents to query real-time and historical analytics from WireBoard, allowing natural language questions about visitors, events, and site metrics.
    Last updated
    10
    57
    3
    MIT
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    Enables LLM clients to interact with OpenSearch Dashboards, including reading saved objects, listing tenants and index patterns, and querying logs through the Dashboards API.
    Last updated
    5
    MIT
  • F
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    quality
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    maintenance
    A Model Context Protocol server that gives LLM clients full access to your AppDynamics monitoring data — plus the ability to create and manage dashboards and health rules.
    Last updated
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    4
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    Session Watcher treats your prompt cache as inventory — it uses EOQ theory to tell you whether the current context is still worth carrying, and when to restart. Works with any session-based coding agent.
    Last updated
    3
    782
    1
    MIT
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    Enables causal root cause analysis with 55 tools covering causal graph construction, 13 RCA model families, multi-model consensus, and report generation. Works with 9 LLM providers including Claude, Ollama, and OpenAI.
    Last updated
    56
    MIT
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    Enables LLM agents to search and analyze Elasticsearch logs for errors, detect recurring patterns, analyze error-rate trends, and retrieve full trace context through MCP tools.
    Last updated
    6
    MIT
  • F
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    Estimates the environmental footprint of your AI use — energy (kWh), miles driven, water used for cooling, and CO₂ — plus a prompt-efficiency score, working with any AI client by measuring token usage.
    Last updated
    9