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"Understanding Inference Models" matching MCP servers:

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    MCP server for achieving peak inference performance by detecting drift between code and runtime behavior, comparing against benchmarks, and providing optimization templates.
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    Apache 2.0
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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
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    MIT
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    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.
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    Apache 2.0
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    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.
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    MIT
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    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.
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    MIT
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    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.
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    BSD 3-Clause
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    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.
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    MIT
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    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.
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    MIT
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    A Model Context Protocol (MCP) server for the Pingera monitoring service, providing seamless integration between AI models and monitoring data.
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    MIT
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    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.
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    MIT
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    MCP server for querying observability data from Elasticsearch, SkyWalking, and Prometheus/VictoriaMetrics, enabling AI models to search logs, traces, and metrics across environments.
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    MIT
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    A TypeScript implementation of a Model Context Protocol server that connects to Sentry error tracking service, allowing AI models to query and analyze error reports and events.
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    MIT