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Glama

Morpha

by ai.morphareels

find_public_image

Search a PUBLIC, Creative-Commons + public-domain image pool (Openverse) for the query, download the top suitable result, store it in the project's asset bucket, and return the filename ready for add_image_layer. This finds someone else's openly-licensed image — it is NOT for uploading your own image (use upload_image for that). Use it for any "get me a parasol" / "find a beach photo" / "add a coffee cup" request — saves the upload-then-add_image_layer dance. Free; no model spend. Falls back to 404 when no suitable downloadable image is found. Always returns attribution { creator, creator_url, title, license, license_version, source_url } per the CC requirement; the editor/caller should surface this where the licence requires it. (Formerly named fetch_image; that name is still accepted as an alias.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesPlain-language search query — e.g. "parasol on a beach", "vintage coffee cup", "sunset over mountains".
projectIdYesProject to add the image to.
license_typeNoOpenverse licence filter. "all-cc" (default) is widest; "commercial" excludes non-commercial licences; "cc0" pins to public-domain dedications.
min_dimensionNoMinimum width or height (whichever is larger) in px. Default 800 so the layer doesn't disappoint at canvas size.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.5/5.0
Behavior5/5

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

The description discloses key behaviors: downloads and stores in the project asset bucket, returns a filename ready for add_image_layer, falls back to 404 when no suitable image, always returns attribution, and is free (no model spend). No annotations are provided, so this full disclosure is essential and well done.

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 somewhat verbose but avoids fluff. It packs useful examples, caveats, cost info, and alias into a single paragraph that remains easy to parse. Slightly long but acceptable for the complexity.

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 return value, side effects, fallback behavior, attribution, and cost. It does not explicitly tie each parameter to usage scenarios, but the schema already provides that context, so the description is sufficiently complete for correct usage.

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?

The schema descriptions already cover all four parameters at 100% coverage. The tool description adds minimal parameter-specific details beyond the schema, so it does not significantly enhance understanding of the parameters themselves.

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's purpose: search a public image pool, download, store, and return a filename for add_image_layer. It also explicitly differentiates from upload_image, making the tool's role unambiguous.

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?

Provides direct usage guidance with concrete examples (e.g., 'get me a parasol'), explicitly states it is NOT for uploading your own image and directs to upload_image, and notes the workflow benefit (saves upload-then-add_image_layer dance).

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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TDQS

A3.6/5.0
Disambiguation3/5

The tool set has several clusters that overlap conceptually: add_keyframe/add_keyframes/set_keyframes_batch, move_band/move_layer/shift_group/set_group_window, and fade_layer/set_layer_transition/set_layer_visible all target similar actions. However, each tool's description is distinct and detailed enough for an agent to disambiguate after careful reading. The sheer volume of 95 tools still creates meaningful selection risk.

Naming Consistency4/5

Tool names overwhelmingly follow a verb_noun snake_case pattern (add_, remove_, set_, list_, rename_, create_, delete_), which is predictable and readable. Minor inconsistencies exist: plural variants like add_keyframes vs add_keyframe, mixed specificity like remove_keyframe vs remove_color_keyframe, and a few oddballs like reid_project and clip_processing_status. Overall the pattern is strong and helps navigation.

Tool Count2/5

95 tools is far beyond the well-scoped range and will overwhelm agents; even a comprehensive video editor could consolidate keyframe batch operations, overlay/track manipulations, and page/group window controls. Many tools are highly specific (reid_project, safe_zones, move_band) and place burden on the agent to pick among near-synonym verbs. While the broad domain justifies many operations, this count is excessive for a coherent tool surface.

Completeness3/5

The surface covers a broad editing lifecycle: layers, keyframes, text, captions, audio, pages, groups, versions, projects, workspaces, and collections, including batch operations. However, there is no MCP tool to render/export the final MP4 (explicitly delegated to an SDK) and no upload_video tool for bringing in a clip file, leaving major end-to-end gaps. For the stated purpose of building and editing videos, these are significant omissions that force agents to stop short of delivery.

Resources