Fetch a named experiment's full definition: description, parameters with defaults, variants, metrics, and YAML. Use it to review or clone experiment setups.
One experiment: description, every parameter with its default, the variants, the metrics it writes, and its YAML.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. It does useful work by disclosing the return payload (parameters with defaults, variants, metrics, YAML), which is genuinely informative. However, it says nothing about read-only safety, behavior when the name does not exist, or permissions — and the response fields are described rather than the operation's behavior.
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 enumerates the return contents with zero filler. Every clause conveys distinct information about what the agent will receive.
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 and no annotations, the description compensates well by listing the returned fields, which is exactly the gap an absent output schema leaves. It is thin only on input semantics and failure behavior for a simple one-parameter read tool.
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 description coverage is 0% and the single required 'name' parameter is undocumented in both schema and description. The one identifier is self-evident (experiment name), which limits the damage, but the description's phrase 'every parameter with its default' refers to returned experiment parameters, not the input, and could momentarily confuse the agent.
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 names the resource (a single experiment) and enumerates exactly what it returns: description, parameters with defaults, variants, metrics, and YAML. The verb is implied by the name 'get' and the leading 'One experiment:' framing, and it is distinguishable from list_experiments by scope. It is clear but never states the operation explicitly.
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?
There is no explicit when-to-use or when-not-to-use guidance. The phrase 'One experiment' implicitly contrasts with list_experiments (bulk listing) versus fetching full detail for a single named experiment, so usage is inferable but not stated. No prerequisites, no error conditions, no sibling named.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.