Server Quality Checklist
- Disambiguation4/5
Tools are generally well-differentiated by specific resource and action (e.g., move_layer for translation vs reorder_layer for stack position vs transform_layer for scaling). Minor potential confusion exists between apply_adjustment and apply_filter, and between add_fill_layer (creates layer) vs fill_selection (operation), but descriptions clarify intent.
Naming Consistency5/5Excellent consistency throughout: all tools use snake_case with the photopea_ prefix, follow a strict verb_noun pattern (add_layer, delete_layer, get_layers, set_layer_properties), and use parallel terminology for antonyms (group_layers/ungroup_layers, undo/redo).
Tool Count2/5With 34 tools, the server significantly exceeds the typical recommended scope (3-15) for MCP servers. While the domain (image editing) is complex, the sheer number risks overwhelming agents and increases the probability of misselection, despite each tool having a specific purpose.
Completeness4/5Covers document lifecycle, comprehensive layer management (CRUD, grouping, properties), content creation (text, shapes, images), selections, adjustments, filters, and undo/redo. The run_script tool provides an escape hatch for missing functionality. Minor gaps exist (no explicit crop, merge layers, or mask operations), but core workflows are well-supported.
Average 3.5/5 across 34 of 34 tools scored. Lowest: 2.8/5.
See the tool scores section below for per-tool breakdowns.
This repository includes a README.md file.
This repository includes a LICENSE file.
Latest release: v0.1.1
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
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- This server provides 34 tools. View schema
No known security issues or vulnerabilities reported.
This server has been verified by its author.
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Tool Scores
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Glama performs regular codebase and documentation scans to:
- Confirm that the MCP server is working as expected.
- Confirm that there are no obvious security issues.
- Evaluate tool definition quality.
Our badge communicates server capabilities, safety, and installation instructions.
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How to claim the server?
If you are the author of the server, you simply need to authenticate using GitHub.
However, if the MCP server belongs to an organization, you need to first add glama.json to the root of your repository.
{
"$schema": "https://glama.ai/mcp/schemas/server.json",
"maintainers": [
"your-github-username"
]
}Then, authenticate using GitHub.
Browse examples.
How to make a release?
A "release" on Glama is not the same as a GitHub release. To create a Glama release:
- Claim the server if you haven't already.
- Go to the Dockerfile admin page, configure the build spec, and click Deploy.
- Once the build test succeeds, click Make Release, enter a version, and publish.
This process allows Glama to run security checks on your server and enables users to deploy it.
How to add a LICENSE?
Please follow the instructions in the GitHub documentation.
Once GitHub recognizes the license, the system will automatically detect it within a few hours.
If the license does not appear on the server after some time, you can manually trigger a new scan using the MCP server admin interface.
How to sync the server with GitHub?
Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.
To manually sync the server, click the "Sync Server" button in the MCP server admin interface.
How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.