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mlflow-mcp-server

by us-all

search-prompt-optimization-jobs

Search for prompt optimization jobs in a specified experiment, with optional filtering and pagination to locate relevant results.

Instructions

Search prompt optimization jobs in an experiment

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
experimentIdNoExperiment ID to scope the search (defaults to MLFLOW_EXPERIMENT_ID)
filterNoFilter expression
maxResultsNoMax jobs to return
pageTokenNoPagination token
Behavior2/5

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

No annotations exist, so the description must fully disclose behavior. It only states 'Search' without specifying read-only nature, pagination, error handling, or what happens with no results. Minimal behavioral context beyond the tool name.

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

Conciseness4/5

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

Single sentence, front-loaded with the main action and resource. It is concise but lacks details that would not add burden; however, it could include the return type without being verbose.

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?

No output schema exists, and the description omits what the tool returns (e.g., a list of jobs). For a search tool with 4 parameters, the description should mention the result format or that it supports pagination and filtering.

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?

Input schema has 100% description coverage, providing parameter meanings. The description adds no value beyond that, meeting the baseline for high schema coverage.

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

Purpose4/5

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

The description clearly identifies the action ('Search'), the resource ('prompt optimization jobs'), and the scope ('in an experiment'). It distinguishes from sibling 'get-prompt-optimization-job' by implying a list operation, but does not explicitly differentiate.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives like 'get-prompt-optimization-job' (singular) or other search tools. No context on prerequisites or exclusions.

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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