Skip to main content
Glama

Clueso

by io.clueso

Auto-generate sync points

auto_sync
Destructive

Run the agentic auto-sync pipeline against a clip with a source recording. Detects natural sync points (UI state changes, narrated steps) and inserts sync-marker nodes into the clip's transcript.

Async: returns immediately with a status enum from the pre-flight; sync-marker nodes appear in the transcript a few seconds later. Poll get_clip if you need to verify.

Capacity: capped at 3 concurrent runs platform-wide. Returning status='success' means the job was accepted, not that it finished.

Sync points are required input for voiceover TTS on video clips — without them, the TTS has no per-step pacing reference. (See resource clueso://docs/sync-points for the full model.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
clip_idYesClip ID to auto-sync.
project_idYesProject ID.

TDQS

A4.4/5.0
Behavior5/5

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

Adds significant behavioral details beyond annotations: async nature, immediate return of status enum, delayed insertion of sync markers, polling recommendation, and platform-wide concurrency cap. Annotations already indicate destructiveness, but the description enriches understanding.

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?

Approximately 100 words, well-structured into three logical paragraphs. Every sentence adds value: purpose, async behavior, capacity, and usage context. No redundancy or fluff.

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?

Covers purpose, async behavior, concurrency, and usage context. References external docs for more detail. However, does not enumerate possible status enum values returned from the pre-flight, which is a minor gap given no output schema.

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

Parameters3/5

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

Schema coverage is 100% with two parameters. The description does not add any additional meaning beyond the parameter names and brief descriptions in the schema. Baseline score of 3 is appropriate.

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?

Clearly states the verb 'run the auto-sync pipeline' and resource 'clip with source recording'. Distinguishes from sibling 'add_sync_point' by description of automation. The title 'Auto-generate sync points' aligns with the description.

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?

Provides strong context: sync points are required for TTS, and describes capacity limits and async behavior. Implicitly differentiates from manual sync point addition. However, lacks explicit when-not-to-use guidance or alternative tool mentions.

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

All Clueso toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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