Skip to main content
Glama
dlyrr
by dlyrr

capcut_read_project

Reads a CapCut project's full timeline, listing each track and segment with start time, duration, speed, and source material. Enables detailed analysis of project edits.

Instructions

Read one project's timeline: every track, and every segment on it with its start time, duration, speed and the material it plays. Times are in seconds.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
projectYesFolder name exactly as capcut_list_projects reports it.
Behavior4/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It explicitly frames the operation as 'Read', which implies read-only, and details the output fields and time unit. It does not cover error cases or permissions, but the core read behavior is well disclosed.

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 two crisp sentences. The first sentence front-loads the purpose and enumerates the return content, and the second adds the unit. Every word earns its place with no redundancy or ambiguity.

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?

For a single-parameter read tool with no output schema, the description gives a solid high-level picture of what is returned: tracks, segments with start time, duration, speed, and material. It lacks exact nesting details and error behavior, but the description is adequate for the agent's likely needs.

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% because the only parameter 'project' has a highly descriptive schema comment: 'Folder name exactly as capcut_list_projects reports it.' The tool description adds no further parameter semantics, so the baseline of 3 applies.

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 'Read one project's timeline' with a specific verb and resource, then enumerates the exact content returned (tracks, segments, start time, duration, speed, material). This unambiguously differentiates it from siblings like capcut_list_projects and capcut_project_media.

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

Usage Guidelines3/5

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

The description implies its use case by detailing what it reads, but offers no explicit guidance on when to choose it over alternatives or any exclusions. Sibling tools are not referenced, so the agent must infer the appropriate context from the purpose alone.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/dlyrr/capcut-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server