get_datasetRetrieve a dataset's files, passport, and plain-language summary using its author/name ID to review an abliterated LLM's training data. Costs 1 credit.
One dataset with its files and passport, the plain-language summary (GET /datasets/{id}). Cost: 1 credit.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Dataset id as author/name, e.g. mlabonne/orpo-dpo-mix-40k. |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, and openWorld, so the safety profile is covered. The description adds something annotations don't: the 'Cost: 1 credit' quota, which is genuinely useful for an agent budgeting calls. It stops short of 5 by omitting auth requirements or rate limits.
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 that packs the return payload, the underlying endpoint, and the cost with zero filler. Every clause earns its place.
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 output schema, the description compensates by naming what comes back (files, passport, plain-language summary) and the credit cost. That is sufficient for a simple one-param read tool, though it could say a bit more about error behavior for unknown ids.
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?
Schema coverage is 100% and the id parameter is documented with a concrete example (mlabonne/orpo-dpo-mix-40k), so the schema carries the full load. The description adds no parameter detail beyond the schema, making the baseline 3 appropriate.
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?
States a specific verb+resource (get one dataset) and enumerates the returned payload (files, passport, plain-language summary), which cleanly separates it from search_datasets. It doesn't explicitly name the sibling it's not, so it stops short of a 5.
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 implied: fetch a single dataset by id rather than searching. There is no explicit when-to-use/when-not guidance and no mention of the alternative search_datasets for discovery, so it lands at the implied-usage tier.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.