MCP servers and skills: search, filter by where they run, and compare.
1–24 of 39 tools for “arize”
Clear allAgent skills for Arize — datasets, experiments, and traces via the ax CLI
SkillMIT★ 66No key
Adds Arize AX tracing to an LLM application for the first time. Follows a two-phase agent-assisted flow to analyze the codebase then implement instrumentation after user confirmation. Use when the use
OfficialSkillMIT★ 40k in collectionNo key
Creates, reads, updates, and deletes Arize AI integrations that store LLM provider credentials used by evaluators and other Arize features. Supports any LLM provider (e.g. OpenAI, Anthropic, Azure Ope
OfficialSkillMIT★ 40k in collectionNo key
Downloads, exports, and inspects existing Arize traces and spans to understand what an LLM app is doing or debug runtime issues. Covers exporting traces by ID, spans by ID, sessions by ID, and root-ca
OfficialSkillMIT★ 40k in collectionNo key
Creates and manages annotation configs (categorical, continuous, freeform label schemas) and annotation queues (human review workflows) on Arize. Applies human annotations to project spans via the Pyt
OfficialSkillMIT★ 40k in collectionNo key
Generates deep links to the Arize UI for traces, spans, sessions, datasets, labeling queues, evaluators, and annotation configs. Produces clickable URLs for sharing Arize resources with team members.
OfficialSkillMIT★ 40k in collectionNo key
Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI. Use
OfficialSkillMIT★ 40k in collectionNo key
Creates, manages, and queries Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI. Use when the user needs test data,
OfficialSkillMIT★ 40k in collectionNo key
Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and cont
OfficialSkillMIT★ 40k in collectionNo key
Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop
OfficialSkillMIT★ 40k in collectionNo key
OpenTelemetry Instrumentation for AI Observability
SkillApache-2.0★ 1.3kNo key
A tiny MCP stdio server that exposes DeepSeek as a cheap supervised worker for Claude Code, Codex, or any MCP client.
PythonMIT★ 53No key
OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production.
OfficialSkillApache-2.0★ 40k in collectionNo key
Debug LLM applications using the Phoenix CLI. Fetch traces, analyze errors, structure trace review with open coding and axial coding, inspect datasets, review experiments, query annotation configs, an
OfficialSkillApache-2.0★ 40k in collectionNo key
Automate your mobile devices with natural language commands - an LLM agnostic mobile Agent 🤖
Hosted by authorMIT★ 9.7kNo key
🚪 Governance gate for AI agents. Enforce policies before deployment: request contracts, operational safeguards, identity boundaries, action budgets. CLI tool with YAML config, JSON/text output, CI/CD
PythonMIT★ 4Few starsNo key
OpenTelemetry distributed agent span trace annotator & latency profiler (Arize Phoenix)
SkillLink only★ 8
Tamper-proof audit trail for AI agents — the verifiable layer of agent observability. Signed, offline-verifiable receipts for every agent action. TS + Python, MCP server, zero backend. MIT.
Node.jsMITFew starsNo key
Use to summarize a recorded video via the LVS summarization microservice (HITL-gated) with a VLM fallback. Not for report generation or live RTSP captioning.
OfficialSkillApache-2.0★ 3.6k in collectionNo key
Node.jsMIT★ 62No key
MCP server that fetches YouTube video transcripts and optionally summarizes them.
PythonMIT★ 6No key
A Claude Skill that automatically analyzes uploaded CSV files — generating summary statistics, detecting missing data, and creating quick visualizations using Python and pandas.
SkillLink only★ 471
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