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"Importing FastMCP from the mcp.server Python Module" matching MCP servers:

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    Enables MCP-compatible agents to interact with Grafana instances for searching, creating, and updating dashboards, exploring logs via Loki, querying datasources, managing alerts, incidents, and on-call shifts, and accessing observability data.
    8
    Apache 2.0
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    An MCP server that enables AI assistants to interact with Tenzir, a data pipeline engine for security operations, including executing pipeline queries, managing OCSF schemas, and generating parsers.
    15
    Apache 2.0
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    A Model Context Protocol server that enables retrieving and analyzing Sentry issues, including stacktraces and debugging information.
    2
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    Enables natural language interaction with Plausible Analytics data to query traffic, visitors, engagement, and more using conversational questions.
    4
    1
    MIT
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    MCPWatch: observability for MCP servers. One line instruments any Python MCP server (FastMCP and the low-level Server) and tracks per-tool latency (p50/p95/p99), error rates, silent failures (empty/null returns and isError), and call volume. REST API, CLI, and alert hooks. MIT, no config required.
    1
    MIT
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    An AI-powered server that analyzes system log files to identify errors/warnings and recommend fixes using FastMCP, LangGraph ReAct agents, and Anthropic Claude.
    1
    MIT
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    A FastMCP server that provides DataDog log search and monitoring with service-aware filtering, preview-execute workflow, and response size safeguards.
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    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.
    3
    7
    MIT
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    An MCP server that automatically instruments Python AI agents with the ioa-observe-sdk, adding OpenTelemetry-based tracing, metrics, and logs with zero manual effort.
    2
    Apache 2.0
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    Exposes live industrial IoT telemetry to any MCP client, streaming simulated sensor data from a fleet of machines and detecting anomalies, with the ability to inject faults on demand.
    4
    MIT
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    An on-call agent for data incidents, built on a bio-inspired context protocol. It triages data quality and freshness issues using DataHub, computing severity, blast radius, and ownership from the catalogue before generating prose.
    9
    Apache 2.0
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    Enables AI agents to push and read metrics from EzStat, track counters/gauges, and ask natural-language questions about telemetry.
    4
    MIT
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    Enables querying logs, traces, and metrics from multiple OpenObserve instances via MCP tools, with parallel execution, batching, and caching.
    6
    247
    8
    MIT