get_design_guideClueso's house style for composing video clips — how to think about concept, typography, color, composition, motion, and pacing. Read it before composing or editing a clip's visuals when you're NOT following a clueprint, or to fill gaps a clueprint leaves silent. General craft, not clueprint-specific.
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint=true and destructiveHint=false, so the read-only behavior is covered. The description adds value by explaining that the tool provides 'general craft' guidance and describes what the guide covers (concept, typography, color, etc.), which gives the agent a clear expectation of the content. It's nearly perfect; a slight deduction because it doesn't explicitly state that the tool returns a textual guide or that no side effects occur, though these are implied by 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?
The description is three short sentences, each earning its place: first states what it is, second gives usage conditions, third clarifies scope. No redundancy, no filler. It's perfectly sized for the task.
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 the tool has no parameters, clear annotations, and a simple retrieval purpose, the description fully covers what an agent needs: what the tool does, when to use it, and what content it covers. The sibling tool list is long and varied, so the clear usage guidance is essential for disambiguation. The description is complete and leaves no obvious gaps.
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 input schema has zero parameters with 100% coverage, so the baseline is 3. The description adds value by implicitly explaining the scope of the returned content (the 'house style' covering specific design aspects), which helps the agent understand what to expect even though no parameters are needed. The score is elevated because the description compensates for the lack of parameter context by detailing the content domains.
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 retrieves 'Clueso's house style for composing video clips' and specifies the domains it covers ('concept, typography, color, composition, motion, and pacing'). It also distinguishes itself from other tools by clarifying it's for general craft guidance, not clueprint-specific, which helps differentiate from potentially sibling tools that might offer template-specific advice.
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 tells when to use this tool ('before composing or editing a clip's visuals when you're NOT following a clueprint, or to fill gaps a clueprint leaves silent'). This provides precise usage context, including both primary use case and a secondary fallback scenario, which effectively guides the agent's decision among the many sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Most tools have clearly distinct purposes, but there is notable overlap between remove_elements and remove_from_project(target='element'), which both remove elements from a clip. This duplication could cause an agent to misselect. Otherwise, tools like add_clips, add_elements, add_audio, and analyze_audio are well-differentiated.
All tools follow a consistent verb_noun pattern (e.g., add_clips, create_project, get_clip, update_elements). There are no camelCase or mixed conventions. Even compound names like voiceover_batch and auto_sync fit the pattern. This makes the tool set predictable for an agent.
With 40 tools, the set is significantly larger than the 3-15 range that typically earns its place. While the domain of video creation is broad, several tools seem redundant (remove_elements vs remove_from_project) or narrowly scoped (get_design_guide, get_element_schema), inflating the count. The number feels heavy for the apparent scope.
The tool surface covers most lifecycle operations: create, read, update, delete for projects, clips, elements, audio, articles, and clueprints. Minor gaps exist, such as no explicit tool to delete a voiceover (only mute via update_clips) and no folder management beyond listing. Overall, agents can accomplish full workflows with few workarounds.