list_tuningsCatalog of all 43 AgentTune personality tuning files (slug, code, name, one-line blurb), optionally filtered by system. Use it to resolve a user's personality type to the right slug before calling get_tuning.
| Name | Required | Description | Default |
|---|---|---|---|
| system | No | Optional filter: one of the five personality systems. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint: true, so the description adds value by specifying the exact number of files (43) and the fields included in the output, enhancing transparency beyond annotations.
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?
Two sentences, no fluff. The first sentence describes the output, the second gives usage context. Highly efficient and front-loaded.
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?
Given no output schema, the description adequately describes the output structure. It also explains the tool's role in the workflow with get_tuning. Missing details like pagination are not critical for a small fixed dataset.
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% for the single parameter (system), so the description adds no new information beyond stating it is optional. Baseline score of 3 is 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?
The description clearly states the tool lists all 43 AgentTune personality tuning files with specific fields (slug, code, name, one-line blurb), and it distinguishes itself from the sibling get_tuning by indicating it should be used before get_tuning.
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?
The description explicitly says to use this tool to resolve a user's personality type to the right slug before calling get_tuning, providing clear context. It does not mention when not to use it relative to get_test_spec, but the guidance is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: retrieving a test specification, listing available tunings, and fetching a specific tuning file. No overlap or ambiguity.
All tool names follow a consistent 'verb_noun' pattern using snake_case (get_test_spec, get_tuning, list_tunings), making them predictable and easy to understand.
With 3 tools, the server is tightly scoped to its personality tuning domain. Each tool serves a necessary function without redundancy or excess.
The tools cover the complete workflow: retrieve a test spec, list tunings to find the matching slug, and fetch the tuning file. The description includes clear instructions for scoring and installation, leaving no gaps.