Weave MCP Server + Client Linked Traces:
Description
# Weave MCP Server + Client Linked Traces: This repo is taken from the example in Arize-ai/phoenix and adapted to export to wandb/weave. Note: There is a race condition which sometimes causes the tool to fail to run at the OpenAI call step. This bug was also present in the…
About
# Weave MCP Server + Client Linked Traces: This repo is taken from the example in Arize-ai/phoenix and adapted to export to wandb/weave. Note: There is a race condition which sometimes causes the tool to fail to run at the OpenAI call step. This bug was also present in the original and was not introduced by weave. ##…
Details
- Author
- zbirenbaum
- Downloads
- 262
- Categories
- Other
Jump to
- End-to-end tracing across MCP client and server
- Automatic OpenTelemetry context propagation via openinference-instrumentation-mcp
- Exports traces to wandb/Weave
- Supports multi-language MCP components
- Connects AI models to external data sources
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Weave MCP Server + Client Linked Traces:Command (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Configure a .env file with your OPENAI_API_KEY and PHOENIX_COLLECTOR_ENDPOINT (and PHOENIX_API_KEY if required), install dependencies via uv sync or pip install -r requirements.txt, then run uv run client.py or python client.py from the tutorials/mcp/tracing_between_mcp_client_and_server directory. The client spins up the server automatically and you can ask questions to the agent.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"weave mcp server + client linked traces:": {
"weave-mcp-server-client-trace": {
"command": "python",
"args": [
"client.py"
]
}
}
}
}McpServers
{
"weave-mcp-server-client-trace": {
"command": "python",
"args": [
"client.py"
]
}
}Weave MCP Server + Client Linked Traces:
This repo is taken from the example in Arize-ai/phoenix and adapted to export to wandb/weave. Note: There is a race condition which sometimes causes the tool to fail to run at the OpenAI call step. This bug was also present in the original and was not introduced by weave.Set up your environment:
First run cp .env.example .env
Follow the instructions and set the relevant keys in your new env file.
Install Dependencies:
- uv: uv sync
- pip: pip install -r requirements.txt
Run the client and export traces
- uv: uv run client.py
- python: python client.py
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