Mozilla Readability Parser MCP Server
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 |
|---|---|
| parseA | Extracts and transforms webpage content into clean, LLM-optimized Markdown. Returns article title, main content, excerpt, byline and site name. Uses Mozilla's Readability algorithm to remove ads, navigation, footers and non-essential elements while preserving the core content structure. |
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 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool has a clear and distinct purpose focused on parsing webpage content into clean Markdown.
A single tool inherently has perfect naming consistency, as there are no other tools to compare against. The tool name 'parse' is straightforward and appropriate for its function.
A single tool is too few for a server's purpose, even if that purpose is narrow. This limits functionality and makes the server feel thin, as it lacks complementary operations like configuration, validation, or batch processing that might be expected in a parsing domain.
The tool surface is severely incomplete for a parsing server. While the 'parse' tool covers the core extraction function, there are obvious gaps such as no tools for handling errors, validating inputs, managing configurations, or providing metadata about the parsing process, which could lead to agent failures in real-world scenarios.