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"Methods to Store and Learn Dynamic Context Memories" matching MCP servers:

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    Minimal agent telemetry store with OTLP/HTTP ingestion to Postgres and MCP streamable-http tools for querying logs, traces, and metrics.
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    Enables interaction with Google Cloud services including billing cost analysis, log querying, and metrics monitoring through natural language commands. Provides comprehensive tools for managing GCP resources, analyzing costs, detecting anomalies, and retrieving operational insights.
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    Apache 2.0
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    Windows tray process that exposes local ambient context (presence, foreground app, battery, etc.) as MCP tools with privacy classification and opt-in controls.
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    MIT
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    A comprehensive gateway and registry that unifies MCP, REST, and Agent-to-Agent services into a single secure endpoint for AI clients. It provides advanced capabilities including federation, virtual servers, rate-limiting, and OpenTelemetry observability.
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    Apache 2.0
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    A feature-rich gateway and proxy that federates MCP, REST, and gRPC services into a unified endpoint for AI clients. It enables virtualization of legacy APIs as MCP-compliant tools while providing built-in security, rate-limiting, and OpenTelemetry observability.
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    Apache 2.0
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    Enables LLMs to directly query a Matomo analytics instance, execute reporting methods, fetch historical trends, report metadata, and dynamically generated chart images through typed MCP tools.
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    Exposes the complete Zabbix API functionality through the Model Context Protocol, mapping API methods to tools for managing hosts, triggers, and monitoring data. It enables seamless integration and control of Zabbix monitoring environments via natural language interfaces.
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    MIT
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    An MCP server implementation that enables dynamic configuration of OpenTelemetry Collectors, allowing users to add, remove, and configure receivers, processors, and exporters through MCP tools.
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    GPL 3.0
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    Provides read-only access to Jaeger distributed tracing data through the Model Context Protocol. Enables Claude and other MCP-capable agents to search traces, inspect spans, and analyze service dependencies directly within conversations.
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    MIT
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    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.
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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.
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    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.
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    MIT
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    MCP server for log file analysis. Gives LLMs the ability to efficiently analyze large log files without loading them into context.
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    MIT
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    Provides access to Prometheus metrics and queries through standardized Model Context Protocol interfaces, allowing AI assistants to execute PromQL queries and analyze metrics data.
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    MIT
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    A Model Context Protocol (MCP) server for PostgreSQL that provides safe, structured access to your database for AI assistants, enabling health checks, index tuning, lock analysis, and more.
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    MIT
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    A Model Context Protocol server that connects AI agents to live Java processes via Alibaba Arthas, enabling real-time diagnostics, debugging, and performance analysis without code changes.
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    MIT
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    A Model Context Protocol server that enables retrieving and analyzing Sentry issues, including stacktraces and debugging information.
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