Discover supported dataset formats, their detection methods, required optional dependencies, and validation checks to plan dataset validation or conversion.
List supported dataset formats, how each is detected, which optional dependency it needs, and the available validation checks.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the burden, but it discloses the content of the response (formats, detection, dependency, checks), which implicitly signals a safe read-only reference call. It does not state whether output is static/cached or how large the list may be.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single front-loaded sentence with no filler; every clause adds a distinct fact about what is returned.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no parameters, no annotations, and no output schema, the description is the only carrier of semantics, and it fully enumerates the fields an agent will get back. Nothing needed to invoke or interpret the call is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Zero parameters, so there is nothing for the description to disambiguate; baseline 4 applies. The description correctly implies no input is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description pairs a specific verb ("List") with a specific resource ("supported dataset formats") and enumerates exactly what each entry covers: detection method, optional dependency, and validation checks. That content makes it clearly distinct from action-oriented siblings like detect_dataset, validate_dataset, or convert_dataset.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is only implied: an agent can infer this is the reference lookup to consult before calling detect_dataset or validate_dataset, but the description never says when to use it or what it is not for. No alternatives are named.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.