Skip to main content
Glama
us-all

mlflow-mcp-server

by us-all

search-tools

Discover MLflow tools by natural language query. Returns matching tool names and descriptions across categories to navigate the tool surface efficiently.

Instructions

Discover available tools by natural language query. Returns matching tool names + descriptions across all categories. Use this first to navigate the 77+ tool surface efficiently.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural language query. Discover tools across the MLflow MCP surface (experiments, runs, registry, traces, assessments, webhooks, prompts).
categoryNoRestrict search to a specific category
limitNoMax results (default 20)
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It states the tool returns results but does not disclose any side effects, authentication needs, or limitations, which is acceptable for a read-only search tool.

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?

Two sentences, front-loaded with purpose and usage guidance. Every sentence adds value without waste.

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

Completeness4/5

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

For a simple search tool with 3 parameters and no output schema, the description adequately explains what it does and when to use it. It could mention return format explicitly but 'Returns matching tool names + descriptions' is sufficient.

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, so the description adds little beyond the schema. It provides context like 'natural language query' and 'efficiently navigate' but no additional parameter-level details.

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 tool's purpose: 'Discover available tools by natural language query' and specifies it returns tool names and descriptions across categories. It distinguishes from sibling tools which are specific actions.

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?

The description advises 'Use this first to navigate the 77+ tool surface efficiently,' providing clear guidance on when to use it. However, no explicit exclusion or alternatives are mentioned.

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