MLflow MCP Server

info

• This feature is experimental and may change in future releases.
• MLflow 3.4 or newer is required.

The MLflow Model Context Protocol (MCP) server enables AI applications and coding assistants to interact with MLflow traces programmatically. MCP is an open protocol that provides a standardized way for AI tools like Claude, VS Code extensions, and other language models to access external data sources and tools.

The MLflow MCP server exposes all MLflow trace management operations through the MCP protocol, allowing AI assistants to:

• Search and retrieve trace data
• Analyze trace performance and behavior
• Log feedback and assessments
• Manage trace tags and metadata
• Delete traces and assessments

This integration makes it easy to incorporate MLflow tracing capabilities into AI-powered development workflows, enabling more intelligent analysis and management of your GenAI applications.

Prerequisites

• MLflow version 3.4 or newer
• An MCP-compatible client (VS Code, Cursor, Claude, etc.)

Set up

Configure the MLflow MCP server in your MCP client by adding the server configuration to your client's settings file:

VS Code

Add to your VS Code configuration file (.vscode/mcp.json):

{
  "servers": {
    "mlflow-mcp": {
      "command": "uv",
      "args": ["run", "--with", "mlflow>=3.4", "mlflow", "mcp", "run"],
      "env": {
        "MLFLOW_TRACKING_URI": "<MLFLOW_TRACKING_URI>"
      }
    }
  }
}

Cursor

Add to your Cursor configuration file (.cursor/mcp.json):

{
  "mcpServers": {
    "mlflow-mcp": {
      "command": "uv",
      "args": ["run", "--with", "mlflow>=3.4", "mlflow", "mcp", "run"],
      "env": {
        "MLFLOW_TRACKING_URI": "<MLFLOW_TRACKING_URI>"
      }
    }
  }
}

Claude

Add to your Claude .claude/settings.json:

{
  "mcpServers": {
    "mlflow-mcp": {
      "command": "uv",
      "args": ["run", "--with", "mlflow>=3.4", "mlflow", "mcp", "run"],
      "env": {
        "MLFLOW_TRACKING_URI": "<MLFLOW_TRACKING_URI>"
      }
    }
  }
}

Replace <MLFLOW_TRACKING_URI> with your MLflow tracking server URL:

• Local server: http://localhost:5000
• Remote server: https://your-mlflow-server.com
• Databricks: Set the tracking URI to databricks and configure authentication using environment variables such as DATABRICKS_HOST and DATABRICKS_TOKEN. For detailed setup instructions, refer to the Databricks authentication guide.

Available Tools

The MLflow MCP server provides comprehensive trace management capabilities:

Tool	Description	Key Parameters
search_traces	Search and filter traces in experiments	experiment_id, filter_string, max_results, extract_fields
get_trace	Get detailed trace information	trace_id, extract_fields
delete_traces	Delete traces by ID or timestamp	experiment_id, trace_ids, max_timestamp_millis
set_trace_tag	Add custom tags to traces	trace_id, key, value
delete_trace_tag	Remove tags from traces	trace_id, key
log_feedback	Log evaluation scores or judgments	trace_id, name, value, source_type, rationale
log_expectation	Log ground truth labels	trace_id, name, value, source_type
get_assessment	Retrieve assessment details	trace_id, assessment_id
update_assessment	Modify existing assessments	trace_id, assessment_id, value, rationale
delete_assessment	Remove assessments	trace_id, assessment_id

Field Selection and Filtering

The MCP server supports sophisticated field selection through the extract_fields parameter, available in both search_traces and get_trace tools. This parameter accepts comma-separated field paths using dot notation, allowing you to retrieve only the data you need, reducing response size and improving performance. The extract_fields parameter lets you:

• Select specific fields from trace data instead of retrieving everything
• Use wildcards (*) to select all items in arrays or objects
• Combine multiple field paths in a single request
• Use backticks for field names containing dots

Example usage with tools:

# With search_traces
search_traces(
    experiment_id="1",
    extract_fields="info.trace_id,info.state,data.spans.*.name",
)

# With get_trace
get_trace(
    trace_id="tr-abc123",
    extract_fields="info.assessments.*,info.tags.*",
)

Common Field Patterns

Trace Information:

• info.trace_id: Unique trace identifier
• info.state: Trace status
• info.execution_duration: Total execution time
• info.request_preview: Truncated request preview
• info.response_preview: Truncated response preview

Tags and Metadata:

• info.tags.*: All trace tags
• info.tags.mlflow.traceName: Trace name
• info.trace_metadata.*: Custom metadata fields

Assessments:

• info.assessments.*: All assessment data
• info.assessments.*.feedback.value: Feedback scores
• info.assessments.*.source.source_type: Assessment sources

Span Data:

• data.spans.*: All span information
• data.spans.*.name: Span operation names
• data.spans.*.attributes.mlflow.spanType: Span types (AGENT, TOOL, LLM)

Field Selection Examples

# Get basic trace info
info.trace_id,info.state,info.execution_duration

# Get all assessments
info.assessments.*

# Get feedback values only
info.assessments.*.feedback.value

# Get span names
data.spans.*.name

# Get trace name (use backticks for dots in field names)
info.tags.`mlflow.traceName`

Use Cases and Examples

Debugging Production Issues

Use the MCP server to quickly identify problematic traces:

User: Find all failed traces in experiment 1 from the last hour
Agent: Uses `search_traces` with `filter_string="status='ERROR' AND timestamp_ms > [recent_timestamp]"`

Performance Analysis

Analyze execution patterns and bottlenecks:

User: Show me the slowest traces in experiment 2 with execution times over 5 seconds
Agent: Uses `search_traces` with `filter_string="execution_time_ms > 5000"` and `order_by="execution_time_ms DESC"`

Quality Assessment Workflow

Log and manage trace evaluations:

User: Log a relevance score of 0.85 for trace tr-abc123 with rationale about accuracy
Agent: Uses `log_feedback` with appropriate parameters

Data Cleanup

Remove old or test traces:

User: Delete traces older than 30 days from experiment 1
Agent: Uses `delete_traces` with timestamp-based filtering

Environment Configuration

The MCP server respects standard MLflow environment variables:

• MLFLOW_TRACKING_URI: MLflow tracking server URL
• MLFLOW_EXPERIMENT_ID: Default experiment ID
• Authentication variables for cloud providers (AWS, Azure, GCP)

For Databricks environments, ensure you have appropriate authentication configured (personal access tokens, service principals, etc.).

Related Documentation

• MLflow Tracing Overview
• Collecting User Feedback
• Model Context Protocol Specification