io.github.RudrenduPaul/agent-observability
AI agent observability with deterministic record/replay for debugging agent failures.
MCP server · by RudrenduPaul
About
io.github.RudrenduPaul/agent-observability is an MCP server published by RudrenduPaul. Marketplaces we read that list it: 1 (Official MCP Registry). We first saw it on 2026-10-11 and have recorded no change since. As of our marketplace pass on 2026-10-13.
What it asks for
- Runs on your machine
None of the marketplaces we read publishes its tools or the settings it asks for.
What it declares, asks for and runs, in full
Only install plugins from developers you trust. We list what marketplaces publish and do not test plugins.
Package
PyPI agent-observability-trace-cli (agent-observability-trace-cli on PyPI)
- PyPI downloads/week: 39 (2026-10-13)
- PyPI downloads/month: 216 (2026-10-13)
- Latest version on PyPI: 0.1.11, published 2026-08-10
No known vulnerabilities in OSV for 0.1.11 (checked 2026-10-13)
PyPI facts come from pypi.org, PyPI downloads from pypistats.org. Each figure is the registry's own and is never added to another. A registry that publishes no figure shows none: no number is not zero. Vulnerability records come from OSV.dev (CC BY 4.0) and concern the declared version only; a listing that declares no version gets no answer. We do not scan code.
Works with
- Claude Code
- Codex
- Cursor
- VS Code
- Copilot CLI
A one-click link opens the client, which asks you to confirm before it adds the server.
From the marketplaces that list it and the MCP transport it declares, as each client documents them. We do not test plugins.
Connect
Commands and configs built only from what its marketplaces declare. We do not run them for you and do not test plugins.
As a local MCP server: PyPI package
Built from what Official MCP Registry declares.
claude mcp add --transport stdio agent-observability -- uvx --from agent-observability-trace-cli agent-observability-trace-cli agent-trace-mcp[mcp_servers.agent-observability]
command = "uvx"
args = ["--from", "agent-observability-trace-cli", "agent-observability-trace-cli", "agent-trace-mcp"]{
"mcpServers": {
"agent-observability": {
"command": "uvx",
"args": [
"--from",
"agent-observability-trace-cli",
"agent-observability-trace-cli",
"agent-trace-mcp"
]
}
}
}Where it is published
| Marketplace | Status | Version | Figures | Verified | First seen |
|---|---|---|---|---|---|
| Official MCP Registry (Open the registry record) | Listed | 0.1.11 | Downloads/month (PyPI): 216 Downloads/week (PyPI): 39 Forks (GitHub): 0 Stars (GitHub): 0 |
Not marked |
Each figure is the marketplace's own and is never added up across marketplaces. A marketplace that publishes no figure shows none: no number is not zero.
What changed
Nothing has moved on this plugin since we started watching it.
Similar plugins
Products whose own names, descriptions, tools and skills are closest in meaning to this one, on every marketplace we read. Closeness is ranked within this list only.
-
agent-replay io.github.mdfifty50-boop/agent-replay
Record and replay AI agent execution for debugging
-
agent-replay-debugger-mcp io.github.CSOAI-ORG/agent-replay-debugger-mcp
Agent Replay Debugger MCP — record every agent step + deterministic replay. Step-debugger for
-
trace-forge io.github.mdfifty50-boop/trace-forge
Structured execution trace and span logging for AI agents
-
agent-observability io.github.KryptosAI/agent-observability
Trace AI agent execution: every tool call, every error, every dollar. Open source, local-first.
-
observability-mcp io.github.ThoTischner/observability-mcp
Unified observability gateway for AI agents — Prometheus, Loki & more, with anomaly detection.
-
pramana-mcp io.github.loopg/pramana-mcp
Read your recorded AI agent runs, and what changed when you swapped a model or prompt.
-
mcp-server-agent-analytics io.github.joepangallo/mcp-server-agent-analytics
Track AI agent fleet performance, costs, anomalies, and trends.
-
Structured observability for AI agents. Trace steps, decisions, and errors. MCP server + CLI viewer.
-
mcp-server io.github.agentmark-ai/mcp-server
Inspect AgentMark traces and drive AgentMark Cloud: apps, experiments, metrics, deployments.
-
statecli io.github.charo360/statecli
State replay and self-debugging for AI agents. Track, replay, undo, checkpoint.
Similar plugins by marketplace
The same question asked of one marketplace at a time: is there an analog of this plugin there?
Has a listing on Claude Code: claude-plugins-community (5)
-
evalview-mcp io.github.hidai25/evalview-mcp
Regression testing for AI agents. Golden baselines, CI/CD, LangGraph, CrewAI, OpenAI, Claude.
-
vynn-mcp io.github.beee003/vynn-mcp
Self-improving AI workflows with backtesting, prompt optimization, and scheduling
-
deepmiro io.github.kakarot-dev/deepmiro
Simulate hundreds of AI agents to predict how communities react to events and policies
-
agent-discover io.github.keshrath/agent-discover
Find, install and enable MCP servers mid-session on any host, with consent and tool pinning
-
Agent Trace Triage is a Claude Code plugin for developers building agentic AI systems (LangGraph, CrewAI, custom tool loops, MCP agents). When an agent run fails, stuck in a loop…
Has a listing on Claude Code: claude-plugins-official (5)
-
Langfuse observability plugin for Claude Code — captures and exports traces, spans, and session telemetry from Claude Code to Langfuse for LLM monitoring, debugging, and evaluation
-
Detect an MCP server's language (Python, TypeScript or Go) and instrument it with the matching Pendo SDK so MCP tool calls flow into Pendo Agent Analytics.
-
Skills for tracing, evaluating, and improving AI agents with MLflow. Supports the full agent improvement loop: instrument → trace → evaluate → iterate → validate.
-
Dynatrace observability skills: DQL query patterns, application and infrastructure monitoring, log analysis, problem investigation, and incident response workflows, with the…
-
Detect AI agents in your codebase and instrument them with Pendo Agent Analytics via the best-fit path: Python SDK, TypeScript SDK, client-side trackAgent, or the Conversations…
Has a listing on Claude: knowledge-work-plugins (5)
-
Skills for working with Langfuse, the open-source LLM engineering platform for tracing, prompt management, and evaluation.
-
Official SigNoz plugin for MCP setup, docs, queries, dashboards, and alerts
-
Hosted MCP server for AI-assisted Grafana Cloud observability — no local installation required.
-
DataRobot skills for AI/ML workflows — model training, deployment, predictions, feature engineering, monitoring, explainability, data preparation, App Framework CI/CD, and…
-
Use Datadog directly in Claude Code through a preconfigured Datadog MCP server. Query logs, metrics, traces, dashboards, and more through natural conversation. This plugin is in…
Has a listing on Codex: openai-curated (openai/plugins) (5)
-
Skills for NVIDIAs ecosystem spans GPU acceleration, CUDA, AI agents, inference, robotics, Physical AI, Omniverse, and simulation. This plugin helps you understand the pieces…
-
Available for US1 customers only. Analyze, investigate, and act on your Datadog telemetry directly from ChatGPT using natural language. Ask questions about your production…
-
Evaluate Codex skills and plugins from chat with a beginner-friendly start command, local-first reports, token budget explanations, and guided benchmarking.
-
CrowdStrike Falcon Foundry development skills for building cybersecurity applications on the Falcon platform. Includes UI development, collections, functions, workflows, API…
-
Inspect recent issues and events in Sentry from Codex.
Has a listing on Community GitHub marketplaces (5)
-
4da-mcp-server io.github.4DA-Systems/4da-mcp-server
Upgrade intelligence for AI agents: what an upgrade changes, where it touches your code, CVE scans.
-
Dash0 Observability
OpenTelemetry observability for OpenAI Codex sessions. Captures prompts, tool calls, MCP calls, sub-agent activity, and token usage as OTel traces.
-
Dynatrace observability skills: DQL query patterns, application and infrastructure monitoring, log analysis, problem investigation, and incident response workflows, with the…
-
touchdesigner-mcp-server io.github.8beeeaaat/touchdesigner-mcp-server
MCP server for TouchDesigner - Control and operate TouchDesigner projects through AI agents
-
inquisitor io.github.1111111111111111111114oLvT2/inquisitor
Hypothesis-driven problem solving for AI agents: probe, falsify, escalate.
Has a listing on Copilot CLI: github/awesome-copilot (5)
-
Safe BLE diagnostics, evidence workflows, and guarded automation for AI agents.
-
foundry-hosted-agent-copilotkit
Build CopilotKit + AG-UI web apps on Microsoft Foundry hosted agents (Microsoft Agent Framework): wiring, native Foundry primitives, human-in-the-loop approvals, azd deploy, and…
-
Use Datadog directly in Copilot / VS Code through a preconfigured Datadog MCP server. Query logs, metrics, traces, dashboards, and more through natural conversation.
-
Three-agent workflow for orchestrated software delivery with an orchestrator plus implementation and QA subagents.
-
Skills and interactive Copilot canvas for designing, configuring, testing and deploying agents to Microsoft Foundry.
Has a listing on Copilot CLI: github/copilot-plugins (5)
-
Toolkit for building Microsoft 365 Copilot declarative agents — scaffolding, JSON manifest development, and capability configuration.
-
Bring Azure DevOps work-item, repository, and pull-request workflows into GitHub Copilot.
-
mcp-apps
Generate MCP App widgets for MCP tools. Produces self-contained HTML widgets using the MCP Apps protocol.
-
Advanced Security plugin for GitHub Copilot.
-
power-automate
Build, edit, run, and debug Power Automate cloud flows via the FlowAgent MCP server — connection lifecycle, surgical edits, copy across environments, run management, and validated…
Has a listing on Cursor: cursor/plugins (5)
-
Fan large tasks out across parallel cloud agents with planners, workers, verifiers, and structured handoffs.
-
CLI-backed repo compatibility scans plus agents that audit startup, validation, and docs against reality.
-
if you want to go fast, go deep first. pstack helps you write less, but higher quality code. rigorous agent workflows you can parallelize with confidence.
-
Patterns for designing CLIs that coding agents can run reliably: flags, help with examples, pipelines, errors, idempotency, dry-run.
-
Build apps, scripts, and automations with the TypeScript SDK.
Has a listing on Docker MCP Catalog (5)
-
testkube-mcp io.github.kubeshop/testkube-mcp
MCP server for Testkube - Manage test workflows, executions, and artifacts via AI assistants
-
Wayfound’s MCP server allows business users to govern, supervise, and improve AI Agents.
-
MCP server providing Prometheus metrics access and PromQL query execution for AI assistants
-
Dynatrace-mcp io.github.dynatrace-oss/Dynatrace-mcp
[DEPRECATED] Dynatrace MCP Server. Migrate to dynatrace-for-ai or Dynatrace Remote MCP Server.
-
Read and score Opik traces, experiments, datasets and prompts from your AI assistant.
Has a listing on Official MCP Registry (5)
-
agent-replay io.github.mdfifty50-boop/agent-replay
Record and replay AI agent execution for debugging
-
agent-replay-debugger-mcp io.github.CSOAI-ORG/agent-replay-debugger-mcp
Agent Replay Debugger MCP — record every agent step + deterministic replay. Step-debugger for
-
trace-forge io.github.mdfifty50-boop/trace-forge
Structured execution trace and span logging for AI agents
-
agent-observability io.github.KryptosAI/agent-observability
Trace AI agent execution: every tool call, every error, every dollar. Open source, local-first.
-
observability-mcp io.github.ThoTischner/observability-mcp
Unified observability gateway for AI agents — Prometheus, Loki & more, with anomaly detection.
Found by meaning, not by shared code or a shared repository: similar plugins stay separate products, and this is not a review or a recommendation.
Is this your plugin?
Add it to your projects and prove you maintain it with a file in its repository, a file on your domain or a DNS record. You get alerts when a marketplace lists it, drops it, changes its verified mark or what it asks for, and its remote endpoint goes on monitoring as a web service.
Data comes from the Official MCP Registry (registry.modelcontextprotocol.io), CC0 1.0. Figures come from our own regular passes over the marketplaces, last read on . We publish no contact details for plugin publishers.