Outsource MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| outsource_textA | |
| outsource_imageA | |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: one handles image generation while the other handles text generation. Their descriptions explicitly differentiate when to use each tool, with no overlap in functionality or ambiguity about which tool to select for a given task.
Both tools follow a consistent 'outsource_<resource>' naming pattern, using snake_case throughout. The naming convention is predictable and clearly indicates the type of content being outsourced (image vs text).
With only 2 tools, this server feels thin for its apparent scope of 'outsourcing' AI tasks. While the two tools cover image and text generation, the server name suggests broader outsourcing capabilities that aren't represented in the tool surface, such as audio generation, video processing, or other AI services.
For a server named 'Outsource MCP', the tool surface is severely incomplete. It only covers image and text generation, missing obvious outsourcing capabilities like audio generation, video processing, code execution, data analysis, or other AI services that would logically fall under an outsourcing umbrella. The domain implied by the server name is much broader than what's actually covered.