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Clueso

by io.clueso

Update clueprint

update_clueprint
Destructive

Update a clueprint's metadata and/or file contents in one call.

Metadata fields (name, description, tags, visibility, thumbnail_path) are applied as a patch — only the fields you pass change. Pass at least one to update metadata.

files is a list of file mutations:

  • Write text: { path, content }

  • Write binary: { path, content, encoding: "base64" }

  • Write from URL: { path, source_url } (presigned URL, e.g. from get_clip with save=true)

  • Delete: { path, content: null }

You can mix writes and deletes in a single call. Existing files at the same path are overwritten.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoNew name.
tagsNoNew tags (replaces existing).
filesNoFile mutations — writes and deletes. Empty/omitted for metadata-only updates.
visibilityNoNew visibility.
descriptionNoNew description.
clueprint_idYesID of the clueprint to update.
thumbnail_pathNoRelative path of a screenshot in the clueprint to use as thumbnail (e.g. 'screenshots/title-slide.png').

TDQS

A4.5/5.0
Behavior4/5

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

Annotations indicate destructiveHint=true and the description confirms destructive behavior by explaining file overwrites and deletions. It adds valuable behavioral context (patch semantics for metadata, file mutation types) beyond the annotations. Slightly missing details on reversibility but sufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, using bullet points for file mutations and clear sentences for metadata patch behavior. Every sentence adds value, and the structure front-loads the main purpose while organizing details logically. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description comprehensively covers input semantics and behavioral expectations for a mutation tool. It lacks explicit output/return value information, but given no output schema and the tool's nature (update idempotency), the agent can infer success from errors. Minor gap for completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Despite 100% schema coverage, the description adds significant value by grouping parameters (metadata vs files), explaining file mutation patterns with concrete examples (write text, binary, from URL, delete), and clarifying the use of 'content: null' for deletion. This is a substantial improvement over the schema alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states 'Update a clueprint's metadata and/or file contents in one call', using a specific verb and resource. It distinguishes itself from siblings like 'create_clueprint' and 'get_clueprint' by detailing the combined metadata patch and file mutation operations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear guidance on when to use the tool (e.g., metadata patch, file writes, deletes) and how to use different file mutation formats (inline, base64, URL). It lacks explicit mention of when not to use it or alternative tools for specific scenarios, but the context is adequate.

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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TDQS

A4.1/5.0
Disambiguation4/5

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.

Naming Consistency5/5

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.

Tool Count2/5

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.

Completeness4/5

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.

Resources