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Observability

Tools and platforms for comprehensive system observability including logs, metrics, and traces. Enables monitoring, troubleshooting, and performance analysis of applications and infrastructure.

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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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    26,159 PyPI
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    MIT
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    Enables management of PagerDuty incidents, services, schedules, and more directly from MCP-enabled clients, with embedded interactive UIs for incident command center, on-call management, and other features.
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    Apache 2.0
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    Tells an agent what went wrong with its own Ollama calls, in sentences with the fix: lost prompt cache and where the prompt stopped matching, context overflow, a model running partly on CPU, tool schemas crowding the prompt. Also the last turn in numbers and which local models fit in video memory. Local, read-only, nothing leaves the machine.
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    A Model Context Protocol server that lets AI assistants interact with the Sentry API to retrieve and analyze error data, manage projects, and monitor application performance.
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    Records and rewinds AI coding agents (Claude Code, Codex, Cursor, Gemini CLI and others) locally. Its tools take a checkpoint, show the session timeline, preview and apply a rewind with a confirm code, list the tests the agent ran and whether the code changed since, write a handoff, verify the hash-chained log, and check a tool call against your guardrails.
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    Evidence-first MCP server for diagnosing problems with hooks, plugins, skills and configuration, returning ranked probable causes and reviewable fixes without modifying the workspace.
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    Enables AI agents to query OriginChain's telemetry agent for log patterns, semantic incident analysis, distributed traces, service graphs, and read-only SQL queries over telemetry data.
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    Read-only PostgreSQL performance auditing via MCP. Collects metrics from PostgreSQL system views, applies deterministic checks for index, query, vacuum, configuration, and connection issues, and returns structured findings for MCP clients.
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    Checks what local coding agents such as Claude Code, Codex and Cursor really did: files changed, commands run and test results read from test output, not the agent's word. Unclear test results can be double-checked by Laya (on your computer) or TypeSafe's Jev (cloud, your own API key). Read-only.
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    Enables MCP clients to query an organization's Traceorb telemetry data through the same GETs the panel uses, using a read key for authentication.
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    Enables interaction with the Mackerel monitoring platform, providing tools to manage alerts, hosts, dashboards, services, monitors, and traces.
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    319 npm
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    Enables LLMs to query telemetry data via the Spyglass AI agent, providing intelligent insights about application performance, errors, and bottlenecks.
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    Ask your AI agent about your MCP servers: which tools fail, how a release compares, what clients look for and do not find. Reads from mcpspan with a read token.
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    MIT
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    This MCP server enables AI assistants to read PBX state—extensions, DID numbers, SIP trunk providers, tenants, and live statistics—from ICTPBX via the ICTCore REST API, without placing calls or changing configuration.
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    222 npm
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    MIT
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    MCP server that exposes plugin crash reports from a crash reporting site to Claude, enabling querying and managing crash data via tools like listing frequent crashes, searching, adding comments, and managing plugins.
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    MIT
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    Uptime monitoring from your AI agent: create and diagnose checks (40 types: HTTP, TCP, DNS, SSL, ICMP, databases, message queues and more), read results and incidents, and manage status pages and maintenance windows. Built into the self-hostable SolidPing server, served over streamable HTTP at /api/v1/mcp.
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    AGPL 3.0
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    A Linux system monitoring MCP server that provides real-time information on CPU, memory, disk, network, processes, Docker, security, and more via MCP tools.
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    Enables AI assistants to drive the UTMStack SIEM/XDR platform for triaging alerts, searching logs, running SQL, managing incidents, inspecting agents, creating/deleting correlation rules, managing data filters, and running commands on endpoints.
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    MIT
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    Enables data observability operations with the Sifflet platform. Supports exploring assets, monitors, incidents, generating monitor-as-code YAML from descriptions, and performing impact analysis.
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    Enables LLMs to analyze and manage DBOS workflows, including introspection, workflow management, and authentication, to help debug applications in development or production.
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    37 PyPI
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    MIT
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    Provides read-only, governance-audited operations for OLVM and oVirt 4.5 engines, enabling inventory, health, capacity, and diagnosis queries over MCP and CLI with audit logging and budget guards.
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    MIT
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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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