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Valmera — AI Video Editor

by io.valmera
OverviewDiscussionsAdmin

Suggest devices

suggest_devices
Read-only

Returns ranked visual-device candidates for the current program, bound to measured story moments and checked against intensity and flash-safety constraints. Results include explicit candidate edit operations; the request does not apply them.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "project_id": "<project_id>"
    },
    "name": "suggest_devices"
  }
}

Input Schema

NameRequiredDescriptionDefault
endNo
askedNowhat the user explicitly asked for
startNo
familiesNolimit to card families, e.g. matte_fx, punctuation, transition, finish
intensityNoDefaults to the project's current intensity setting.
project_idYesExplicit project identifier for this request. The connection's navigation pointer does not select the target.
max_candidatesNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / intensity / description
      Previous value: -"default: the project's set_intensity"New value: +"Defaults to the project's current intensity setting."
    • changedInput schema / properties / project_id / description
      Previous value: -"Required immutable scope for this call. Copy the id from list_projects/open_project/project_state; the active-project pointer is never used to guess."New value: +"Explicit project identifier for this request. The connection's navigation pointer does not select the target."
  2. Added

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description reinforces that with 'the request does not apply them' plus what the results actually contain (ranked candidates and explicit candidate edit operations). It also discloses two non-obvious behaviors beyond the annotations: candidates are bound to measured story moments and are filtered against intensity and flash-safety constraints. It does not explain why idempotentHint=false (e.g., non-deterministic ranking) or how ranking is ordered.

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?

Two sentences, front-loaded with the return value and scope, followed by the crucial non-mutation caveat. No filler, no restatement of the title.

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

Completeness3/5

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

There is no output schema, and the description partially compensates by describing what results contain, but it omits how many candidates come back by default, what `max_candidates` does to ranking, and whether repeated calls are stable. For a 7-parameter suggestion tool these are meaningful gaps.

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 57%, so roughly half the parameters carry their own documentation. The description adds real meaning for `intensity` and the flash-safety check, and 'bound to measured story moments' hints at the start/end window, but it says nothing about `asked`, `families`, or `max_candidates`, which are left entirely to the schema.

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

Purpose4/5

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

The description gives a specific verb and resource: it returns ranked visual-device candidates for the current program, with the scoping (story moments, intensity/flash constraints) made explicit. It stops short of naming which sibling it replaces, so an agent choosing among suggest_emphasis, suggest_motion_beats, suggest_recipes, or proof_device still has to infer the split from the words 'visual-device'.

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?

Usage is implied rather than stated: 'the request does not apply them' signals this is a read/suggest step that must be followed by an apply-style tool, but no alternative is named and no when-not-to-use condition is given. An agent can infer the workflow position but not the boundary against the other suggest_* tools.

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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