Skip to main content
Glama

Clueso

by io.clueso

Analyze audio

analyze_audio
Read-only

Analyze an audio file. Modes:

  • transcript: Speech-to-text with word-level timestamps. Works on BOTH speech and sung lyrics — routes to a music-aware provider when content is detected as music. Use to get words[{w,start_ms,end_ms}] for caption timing, script editing, or word-level visual sync. Note: features.has_speech may report false on pure music while transcript still successfully extracts lyrics.

  • silences: list of silent regions with start/end/peak. Use for auto-trim, smart-split, or pause-aware editing.

  • beats: tempo (bpm) + beat positions for music tracks. Returns empty for non-music. Use to align animations/zooms to a beat.

  • features: duration, peak/rms/lufs loudness, speech-vs-music heuristic. Cheap dispatcher — call once to decide which other mode to use, or to get duration_ms for source_duration when calling add_audio.

Source: provide exactly one of mcp_upload_id (from upload tool) or file_url (already-public URL, e.g. a find(type='music') result or Clueso CDN file). Optional time range: range_start_ms / range_end_ms crops the analysis window.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYesWhich analysis to run.
file_urlNoPublic http(s) URL to the audio. Use when the file is already accessible (e.g. find(type='music') result, Clueso CDN). Validated against private/loopback ranges.
range_end_msNoEnd of analysis window in milliseconds. Default: whole file.
mcp_upload_idNoMCP upload ID from the upload_file tool. Use this OR file_url, not both.
range_start_msNoStart of analysis window in milliseconds. Default 0 (beginning).
silences_optionsNoOptions for mode='silences'.
transcript_optionsNoOptions for mode='transcript'.

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already set readOnlyHint=true and destructiveHint=false, so the tool is clearly read-only. The description goes beyond this by noting behavioral traits: transcript works on both speech and lyrics by routing to a music-aware provider, features.has_speech may report false on pure music while transcript still extracts lyrics, and beats returns empty for non-music. This adds useful context beyond annotations, though it doesn't cover all edge cases thoroughly.

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 well-structured with a clear header, bullet points for modes, and organized paragraphs for source and optional parameters. Every sentence adds value, and the front-loaded mode descriptions make scanning easy. No wasted words—concise yet comprehensive.

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

Completeness5/5

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

Given the tool's moderate complexity (7 parameters, 1 required, nested objects, 4 modes with specific behaviors) and absence of an output schema, the description is remarkably complete. It covers all modes, edge cases (music handling, empty beats), source options, time range, and notes caveats like false negatives. An AI agent can confidently invoke this tool without additional clarification.

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

Parameters4/5

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

Schema description coverage is 100%, so the baseline is 3. The description adds meaning by explaining how mode values work (e.g., transcript produces words[{w,start_ms,end_ms}]), source constraints, and offering default behaviors (default whole file for range). However, it does not explain all nested options (silences_options, transcript_options) in full detail, lacking a small step to a perfect score.

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 clearly states the tool analyzes audio files and enumerates four distinct modes (transcript, silences, beats, features) with specific use cases. It differentiates from siblings like add_audio (which adds audio to a project) and check_uploads (which checks upload status), and provides enough specificity for an AI agent to understand the scope.

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

Usage Guidelines5/5

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

The description provides explicit guidance on when to use each mode (e.g., 'Use for auto-trim, smart-split' for silences, 'align animations/zooms to a beat' for beats), mentions a cheap dispatcher mode features, and gives alternatives like 'call once to decide which other mode to use'. It also clarifies source options (mcp_upload_id vs file_url) and optional time range, fully satisfying this dimension.

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