ryterpro-mcp
Ryter Pro MCP Server
An MCP server that exposes the Ryter Pro text humanizer API as a tool for MCP-compatible clients.
Tools
humanize_text
Humanize or rewrite text with the Ryter Pro API.
Inputs:
text: Text to humanize.mode: Optional humanizer mode supported by your Ryter Pro API.
ryterpro_api_info
Return basic API configuration and documentation links.
Related MCP server: AI Humanizer MCP Server
Installation
Requires Python 3.10 or later.
pip install ryterpro-mcpConfiguration
Set your Ryter Pro API key before running the server:
export RYTERPRO_API_KEY="your_api_key_here"Optional environment variables:
export RYTERPRO_BASE_URL="https://api.ryter.pro"
export RYTERPRO_TIMEOUT="60"Run
ryterpro-mcpThe server uses the MCP stdio transport by default.
MCP Client Example
{
"mcpServers": {
"ryterpro": {
"command": "ryterpro-mcp",
"env": {
"RYTERPRO_API_KEY": "your_api_key_here"
}
}
}
}For local development from this repository:
{
"mcpServers": {
"ryterpro": {
"command": "python3",
"args": ["-m", "ryterpro_mcp.server"],
"env": {
"RYTERPRO_API_KEY": "your_api_key_here"
}
}
}
}Links
License
MIT
Available Tools
2 toolshumanize_textD
Humanize text using the Ryter Pro API.
Inputs: text (required), mode. Returns: result
ryterpro_api_infoA
Return Ryter Pro MCP server configuration and documentation links.
Inputs: no named parameters. Returns: result
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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TDQS
Scored across 2 tools
The two tools serve clearly distinct purposes: one performs text humanization, the other provides API configuration and documentation. There is no overlap or ambiguity.
Mostly consistent with 'humanize_text' following a verb_noun pattern, while 'ryterpro_api_info' is more noun-oriented. The deviation is minor and both names are clear.
With only 2 tools, the server feels thin for a text-processing API, but the scope is narrow enough that the count is acceptable.
The core functionality of humanizing text is covered, and the info tool provides necessary context. Minor gaps like batch processing or configuration options are not essential for the stated purpose.
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