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"MCP integration with LangChain documentation" matching MCP servers:

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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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    Enables real-time data streaming through Server-Sent Events with timestamp broadcasting and server monitoring capabilities. Optimized for deployment on Render.com with health checks and status endpoints.
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    A Model Context Protocol (MCP) server for interacting with Atlassian Jira. Connect AI assistants like Claude, Cursor AI, and others directly to your Jira projects, issues, and workflows.
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    Provides seamless integration between AI assistants and Prometheus, enabling natural language interactions with your monitoring infrastructure. This server allows for effortless querying, discovery, and analysis of metrics.
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    Enables read-only interaction with NetBox network documentation and infrastructure data through LLMs. Allows querying devices, sites, IP addresses, and viewing change history via natural language.
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    Apache 2.0
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    A Model Context Protocol server that retrieves and analyzes Sentry.io issues, allowing users to inspect error reports, stacktraces, and debugging information from their Sentry account.
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    A comprehensive read-only MCP server for Datadog, providing 117 tools to query logs, APM, metrics, monitors, dashboards, SLOs, and more, with zero write capabilities for safe AI integration.
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    MCP server that interfaces with the Adjust API, allowing you to query mobile analytics reports, metrics, and performance data using natural language from any MCP client like Cursor or Claude Desktop.
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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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    A security layer for AI agents that monitors, checks, and records every action, providing a trust light, timeline, rewind, and ghost mode for testing without consequences.
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    AGPL 3.0
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    Enables interaction with Sentry's error tracking and monitoring tools through a unified API, allowing natural language access to Sentry services.
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    MIT
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    Enables integration with Datadog APIs to monitor and retrieve information about monitors, metrics, dashboards, logs, events, and incidents through the Model Context Protocol.
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    MIT
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    Bridges AI assistants with Wazuh SIEM infrastructure, providing natural language access to security alerts, vulnerability analysis, CVE databases, and network documentation for security operations.
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    MIT