LLM observability and tracing via LangSmith — list projects, query traces, view feedback, and monitor AI runs.
Use with AI AssistantsMCP
Connect Claude, Cursor, or any MCP-compatible client — then call tools directly
① Add This MCP Server
Paste into your AI client config — then all its tools are available instantly.
{
"mcpServers": {
"langsmith": {
"url": "https://mcp.aerostack.dev/s/aerostack/mcp-langsmith",
"headers": {
"Authorization": "Bearer YOUR_AEROSTACK_TOKEN"
}
}
}
}Replace YOUR_AEROSTACK_TOKEN with your API token from the dashboard.
② Call a Tool
Ask your AI assistant to call a specific tool, or send raw JSON-RPC:
Natural Language Prompt
“Use the _ping tool to verify langsmith credentials by calling a lightweight read endpoint. used internally by aerostack to validate credentials”
Using a Workspace?
Add this MCP to your Workspace — your team shares one token, secrets are stored securely, and every AI agent in the workspace can call it without per-user setup.
LLM observability and tracing via LangSmith — list projects, query traces, view feedback, and monitor AI runs.
Live endpoint: https://mcp.aerostack.dev/s/aerostack/mcp-langsmith
This MCP server gives AI agents access to Langsmith via 8 tools. Connect it to any Aerostack workspace and your agents can interact with Langsmith directly.
| Tool | Description |
|---|---|
list_projects | List LangSmith projects (repos/tracing sessions) in your workspace. Each project groups related LLM runs for observability. |
create_project | Create a new LangSmith project for grouping LLM runs and traces. |
list_runs | List LLM runs (traces) in a LangSmith project. Returns inputs, outputs, latency, and token usage for each run. |
get_run | Get detailed information about a specific LangSmith run including full inputs, outputs, error info, and child runs. |
list_datasets | List evaluation datasets in your LangSmith workspace for testing and benchmarking LLM applications. |
create_dataset | Create a new evaluation dataset in LangSmith for storing input/output examples to benchmark your LLM. |
list_examples | List examples (test cases) in a LangSmith evaluation dataset. |
create_example | Add a new input/output example to a LangSmith evaluation dataset for benchmarking. |
| Variable | Required | Description |
|---|---|---|
LANGSMITH_API_KEY | Yes | Your LANGSMITH API KEY from the service's developer settings |
Add the following secrets under Project → Secrets:
LANGSMITH_API_KEYOnce added, every AI agent in your workspace can use Langsmith tools automatically.
curl -X POST https://mcp.aerostack.dev/s/aerostack/mcp-langsmith \
-H 'Content-Type: application/json' \
-H 'X-Mcp-Secret-LANGSMITH-API-KEY: your-langsmith-api-key' \
-d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"list_projects","arguments":{}}}'
MIT
Live Endpoint
https://mcp.aerostack.dev/s/aerostack/mcp-langsmith
Sub-50ms globally · Zero cold start
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Yes. The Langsmith MCP includes a create_project tool that lets Claude create projects in your Langsmith account from a plain-English prompt. You can also update and delete projects — no Langsmith UI needed.
Yes. The Langsmith MCP includes tools to list and retrieve projects from your Langsmith account. Claude can filter, sort, and summarize projects based on your instructions.
Yes. The Langsmith MCP uses the open Model Context Protocol standard, so it works in Claude, Cursor, Windsurf, and any other MCP-compatible AI tool. All 8 Langsmith tools are available everywhere you connect it — install once on Aerostack.
Yes. Aerostack hosts the Langsmith MCP with encrypted credential storage and per-account authentication. Your Langsmith credentials are never shared with Claude's conversation — they're used server-side only.