list_tasksBrowse everyday tasks RealRun has tested AI models on, with item counts, test dates, results pages, and playbook prices to compare model performance.
List the everyday tasks RealRun has tested AI models on, with item counts, test dates, results pages and playbook prices.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds value beyond that by disclosing what each listed item contains (item counts, test dates, results pages, playbook prices), which matters because there is no output schema; it does not cover ordering, pagination, or limits, so it is not a 5.
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 names the action, the resource, and the returned content 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?
For a zero-parameter, read-only list tool with no output schema, the description supplies enough: it identifies the dataset and enumerates the fields returned. Minor gaps remain around result ordering or pagination behavior, but nothing blocks a correct call.
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?
The tool takes no parameters, which is the stated baseline of 4. There is nothing for the description to clarify about inputs, and it correctly does not invent any.
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 gives a specific verb ('List') and a specific resource ('the everyday tasks RealRun has tested AI models on'), plus the fields returned. It is clear what the tool does, but it never distinguishes itself from siblings like get_scores or get_warnings, so an agent gets no routing signal.
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 as a catalog/discovery call is strongly implied by 'list the everyday tasks... with item counts, test dates, results pages and playbook prices', so an agent can infer it is the entry point for browsing. There is no explicit when-to-use, when-not, or mention of the alternatives (get_scores, recommend_model), leaving routing to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.