LangSmith MCP Server (Official)
Official LangSmith MCP server enabling AI agents to interact with LangSmith's LLM observability platform — querying traces, evaluating runs, managing prompts in the Prompt Hub, accessing datasets, and monitoring LLM application performance.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
HTTPS enforced. API key lacks scopes. SOC 2, GDPR. Trace data may contain sensitive content.
⚡ Reliability
Best When
An agent needs to query LangChain/LangSmith traces, manage prompts, or run evaluations in a LangSmith-powered LLM application.
Avoid When
You're using W&B Weave, Arize, or another LLM observability platform.
Use Cases
- • Querying LLM traces and run data for debugging agents
- • Fetching and updating prompts from the Prompt Hub for prompt management agents
- • Running evaluations on datasets for LLM quality assurance agents
- • Monitoring production LLM performance from MLOps agents
- • Analyzing token usage and latency from cost optimization agents
- • Managing evaluation datasets for continuous testing agents
Not For
- • Teams using Weights & Biases, Arize, or other LLM monitoring platforms
- • Teams not using LangChain ecosystem
- • General ML experiment tracking (use W&B or MLflow)
Interface
Authentication
LangSmith API keys at user or service account level. No scope granularity — full project access per API key.
Pricing
Trace-based pricing. Free tier for development. LangSmith is separate from LangChain OSS. MCP server is open source.
Agent Metadata
Known Gotchas
- ⚠ Project name (LANGCHAIN_PROJECT env var) required for trace queries
- ⚠ Run IDs are UUIDs — agents must query by project or filter to find specific runs
- ⚠ Prompt Hub uses separate pull/push API from trace API
- ⚠ Trace retention varies by plan — older traces may not be queryable
- ⚠ API key lacks scope granularity — full org/project access
- ⚠ LangSmith datasets vs trace datasets have different formats
Alternatives
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Scores are editorial opinions as of 2026-03-07.