Skip to main content
Glama

run_intent

Routes natural language video editing requests to the appropriate pipeline for polish, recipe, education, or VFX tasks. Specify intent and optional video path to execute or preview the plan.

Instructions

Route free-text intent to the right pipeline (polish / recipe / education / VFX).

Examples: 'polish talk head for reels with space bg', 'tight cut no filler', 'cyberpunk behind speaker', 'teach Bayes with math plate'. dry_run=true returns the plan only. video_path required to execute.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
intentYes
presetNoyoutube_16x9
bg_modeNonone
dry_runNo
video_pathNo
project_nameNointent_run

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations, the description carries full burden. It discloses the routing behavior, the dry_run mode, and the requirement of video_path for execution. Missing details on permissions or side effects, but the core behavioral traits are clear.

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

Conciseness4/5

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

The description is concise with two lines and examples. It front-loads the core action. While compact, it could use bullet points for clarity, but it avoids unnecessary 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 covers the main functionality and modes adequately. With an output schema present, return values are not required. Missing parameter details for preset, bg_mode, and project_name reduce completeness slightly.

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 0%, so the description must add meaning. It explains 'intent' with examples, 'dry_run' mode, and 'video_path' requirement. However, 'preset', 'bg_mode', and 'project_name' lack any explanation, leaving half of the 6 parameters undocumented.

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 verb 'Route' and the resource 'free-text intent', listing specific pipelines (polish, recipe, education, VFX). Examples illustrate the intent format, making the purpose distinct from sibling tools.

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 examples and explains the dry_run mode for planning versus execution. However, it does not explicitly contrast with sibling tools like 'apply_recipe' or 'plan_cuts', leaving some ambiguity about when to choose this tool.

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/dhirajlochib/VidMcp'

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