Give your AI coding assistant instant access to the full IMAGIN.studio documentation — CDN configuration, API references, integration guides, and more.
One tool. One command. Works with every major AI coding assistant.
Paste this into your agent's MCP config:
{
"mcpServers": {
"imagin-docs": {
"command": "uvx",
"args": ["imagin-studio-api-docs-mcp"]
}
}
}Or just ask your AI assistant:
Install this MCP server: https://pypi.org/project/imagin-studio-api-docs-mcp/
Related MCP server: @ragrabbit/mcp
Install — uvx imagin-studio-api-docs-mcp (no cloning, no venv, no config)
Index — On first run, clones the docs and builds a local vector index (~30 sec)
Search — Your AI assistant calls search_docs to find relevant documentation
Stay fresh — The index auto-updates when the upstream docs change
Everything runs locally. No API keys. No external services.
Agent | Config location |
Claude Code |
|
Claude Desktop |
|
Cursor | Settings > Tools & MCP |
Windsurf |
|
VS Code + Copilot |
|
Cline | MCP servers panel |
Zed |
|
For detailed setup instructions for each agent, see the full setup guide on PyPI.
If your agent can't find uvx (common with GUI apps like Claude Desktop and Cursor):
{
"mcpServers": {
"imagin-docs": {
"command": "npx",
"args": ["-y", "@imagin.studio/api-docs-mcp"]
}
}
}Once installed, try prompts like:
"Search the IMAGIN docs for CDN cache invalidation"
"How do I set up a custom domain with IMAGIN?"
"Find the API endpoint for image transformations"
"What image formats does IMAGIN.studio support?"
Registry | Package | Install |
PyPI |
| |
npm |
|
Apache License 2.0 — see LICENSE.
Search the official IMAGIN.studio technical documentation, integration guides, and knowledge base.
Use this tool when the user asks 'How do I...' questions, needs explanation on API concepts (CDN, referrers, caching, 360 spinner), or needs to debug integration issues. Rewrite vague queries into specific technical search terms before calling.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Natural language search query. Use specific technical terms rather than vague descriptions. Good: "CDN cache invalidation headers". Bad: "caching stuff". | |
| top_k | No | Number of results to return (1-20, default 5). Use 1-3 for focused lookups, 5 for general questions, 10-20 for broad research. |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint true, idempotentHint true, destructiveHint false. The description adds that it searches specific content types but does not discuss rate limits, authentication, or result format. It adds some context but not rich behavioral detail.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, no redundant words. First sentence states purpose, second gives usage examples, third provides query rewriting advice. Front-loaded and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only search tool with good annotations and output schema, the description covers when to use and what to search. It could mention authentication scope or result limitations, but overall it's reasonably complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with detailed descriptions for both parameters. The description does not add per-parameter details beyond the schema, so baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches official IMAGIN.studio technical documentation, integration guides, and knowledge base. This is specific and complete, with no sibling tools to distinguish from.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says when to use ('How do I' questions, API concepts, debug issues) and provides guidance to rewrite vague queries into specific terms. This is optimal usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.23search_docsWith only one tool, there is no possibility of confusion between different tools. The single tool is clearly described for its specific purpose.
The single tool name 'search_docs' follows a consistent verb_noun pattern, which is clear and predictable.
One tool is minimal and may feel insufficient for a documentation set; however, it might be acceptable if the scope is strictly limited to search. Still, it falls into the 'thin' category.
The tool covers the core search functionality, but lacks additional operations like retrieving a specific document or listing available topics, which could be useful for a documentation server.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
AI-native digital asset management: semantic search, generative image edits, and CDN delivery.
Upload, organize, search, and transform images, videos, and files with AI-powered tools.
Search the SiteGPT documentation: setup, features, API reference, troubleshooting.
Car logo API and image CDN: search automotive brands, fetch brand data, and build logo CDN URLs.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/IMAGIN-studio/api-docs-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server
