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    An MCP server that provides cost and reliability observability for LLM and agent workflows. It records model calls and allows querying and aggregating telemetry data through MCP tools.
    6
    MIT
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    An MCP server that emits OpenTelemetry traces, metrics, and logs to OTLP endpoints in various formats. It includes tool-mimicry profiles to generate realistic traffic resembling common tools like nginx, postgres, and AWS Lambda for testing and demonstration purposes.
    11
    MIT
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    Enables AI assistants to query and analyze AI agent sessions from observability providers like Shepherd (AIOBS) and Langfuse, allowing users to debug agent runs, compare sessions, track performance, and analyze LLM usage patterns.
    18
    MIT
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    Provides agent-native analytics and logging for coding agents, enabling product analytics, error logging, and deploy attribution through an MCP handoff.
    1
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    MIT
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    InfluxDB-v1-MCP is a powerful Model Context Protocol (MCP) interface specifically designed for InfluxDB v1.x, enabling AI assistants to intelligently manage and query time-series databases.
    Apache 2.0
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    Enables AI agents to query Microsoft Copilot Studio telemetry and governance data in plain English, pulling from multiple Azure and M365 APIs into a local SQLite database.
    MIT
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    A Postgres MCP server that enables AI agents to safely access production databases through deny-by-default YAML policies, PII masking, row limits, and required predicates. It also provides DBA capabilities like index tuning, health checks, and EXPLAIN plans, with support for multiple databases.
    MIT
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    Enables monitoring and querying of Datadog metrics for Kubernetes clusters, APM traces, infrastructure hosts, and databases through a unified interface.
    10
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    Enables secure enterprise AI agents to access internal tools like GitHub, Gmail, Calendar, file systems, databases, and knowledge bases through the Model Context Protocol, with built-in security, audit, and observability.
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    A local-first MCP server that gives AI coding agents runtime visibility and AI-managed debug logging. It replaces blind print() debugging by turning runtime execution into causal chains, allowing agents to instantly locate bugs by finding missing .success events in Python and TypeScript code. Single binary with MCP, CLI, and HTTP interfaces.
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    Enables coding agents to read performance findings from sixty, get detailed evidence for each finding, and close or resolve them with descriptions, while also checking service telemetry and providing installation instructions.
    6
    23
    MIT
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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