agenttune

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, and the description confirms by stating 'Fetch' and explaining the output format. It adds value detailing the Markdown content and the flow for administration.

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?

The description is a single paragraph but contains all necessary information. It is front-loaded with the main purpose. Slightly longer than necessary but no redundant sentences.

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 simple tool with one parameter and no output schema, the description covers the output contents, usage flow, and test options comprehensively. It even notes the mapping for big-five to ocean files.

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% with a minimal description for the 'test' parameter. The description enriches it considerably by listing each test with its abbreviation, item count, and typical duration, providing essential context for selection.

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 specifies the verb 'Fetch' and the resource 'test specification', listing exact contents (items, scale, algorithm, mapping). It distinguishes from sibling tools by instructing to call 'get_tuning' after scoring.

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 states when to use this tool (to get a test spec) and what to do after (administer, score, then call get_tuning). Sibling tools are named, providing clear guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations declare readOnlyHint=true and openWorldHint=false, consistent with a fetch operation. The description adds context about the output structure (front-matter with install metadata and probe, body as system-prompt content) and MIT licensing, going beyond annotations. No contradictions.

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 with no fluff. First sentence states the core action and format. Second sentence explains the front-matter and license. Information is front-loaded and every sentence 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 simple read-only tool with two parameters and no output schema, the description adequately explains what the returned Markdown contains and its structure (front-matter metadata, body). No mention of size limits or error cases, but overall complete for typical use.

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% with descriptions for both parameters. The tool description does not add meaning for parameters beyond the schema; it focuses on the output. The slug description includes a helpful note about calling list_tunings if unsure, which is a minor addition.

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?

Title and description clearly state 'Get a tuning file (paste-ready Markdown)' and 'Fetch one tuning file as Markdown with YAML front-matter.' It specifies the resource (tuning file), action (fetch), and format (Markdown). Distinguishes from siblings: list_tunings lists all, get_test_spec gets test spec.

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?

No explicit when-to-use or when-not-to-use in the main description. However, the slug parameter description suggests 'Unsure? Call list_tunings,' providing some guidance. Sibling tools are listed but no comparative usage advice.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, so the description's 'catalog' term aligns. It adds context about optional filtering by system and the count of 43 files, which is useful beyond annotations. No contradictions; could mention if results are sorted or if there's pagination, but not required for this small set.

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?

Extremely concise: two sentences that front-load the purpose and usage. No redundant or superfluous words; every phrase serves a clear informative function.

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 simple list tool with one optional parameter and no output schema, the description is fairly complete: it names returned fields, explains the use case, and mentions filtering. Minor gaps like response sorting or list limit are not critical given the small fixed number of tunings.

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% with a detailed enum and description. The description adds that the filter is 'optional' and mentions 'five personality systems' (enum count), but these are minor additions. No parameter details beyond what schema provides.

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 catalogs all 43 AgentTune personality tuning files with fields (slug, code, name, blurb) and can be filtered by system. It distinguishes from sibling tools get_tuning and get_test_spec by explaining its role as a precursor to 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 instructs to use this tool to resolve a user's personality type to the right slug before calling get_tuning, providing clear workflow guidance and avoiding direct use of get_tuning without the slug.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.