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mlflow-mcp-server

get-trace-attachment

Read-only

Retrieve a trace attachment using trace ID and attachment ID. Compatible with Databricks MLflow (OSS servers return 404).

Instructions

Get a specific attachment on a trace by ID (Databricks MLflow only — OSS servers return 404)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
traceIdYesTrace ID
attachmentIdYesAttachment ID
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds the important behavioral trait that OSS servers return 404, which is not covered by annotations. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence that front-loads the action and includes a critical platform note. Every component earns its place without extraneous content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has no output schema, but the description does not explain what is returned (e.g., file content, URL). It also lacks guidance on how to obtain the parameter values (traceId from get-trace, attachmentId from list-trace-attachments). This leaves the agent with incomplete context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% with basic descriptions ("Trace ID", "Attachment ID"). The description does not add extra meaning beyond the schema, so baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action (Get), resource (attachment on a trace), and the unique constraint (Databricks MLflow only). This distinguishes it from siblings like list-trace-attachments and get-trace.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Description explicitly notes the tool is for Databricks MLflow and warns that OSS servers return 404, providing clear context. It does not explicitly mention when to use alternatives, but the platform-specific binding serves as a usage guide.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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