Skip to main content
Glama
us-all

mlflow-mcp-server

by us-all

search-experiments

Search MLflow experiments using filter expressions, pagination, sorting, lifecycle stage filters, and field extraction to reduce response tokens.

Instructions

Search experiments with filter and pagination

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filterNoFilter expression, e.g. "name LIKE '%demo%'"
maxResultsNoMax results (default 100)
orderByNoSort fields, e.g. ['name ASC']
pageTokenNoPagination token
viewTypeNoLifecycle stage filter
extractFieldsNoComma-separated dotted paths with `*` wildcard (e.g. 'experiments.*.experiment_id'). Reduces response tokens.
Behavior2/5

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

With no annotations, the description carries full burden but only states 'search', not behavioral traits like read-only, rate limits, or response format. It fails to disclose if results are sorted, paginated per default, or any side effects.

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?

The description is very concise at one sentence, but lacks structure (no bullet points or examples). It is efficient but could be more informative 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?

Given 6 parameters with full schema descriptions but no output schema or annotations, the description is too minimal. It does not explain how to combine filters, use pagination tokens, or typical use cases.

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%, so parameters are well-documented in the schema. The description adds no additional meaning beyond what the schema already provides. Baseline score of 3 is appropriate.

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 states the tool searches experiments with filter and pagination. It differentiates from sibling tools like search-runs by specifying the resource type. However, it lacks details on what specific fields are searchable.

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 is provided on when to use this tool versus alternatives like search-runs or search-logged-models. The description does not mention use cases or exclusions.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/us-all/mlflow-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server