Read-Website
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| XBY_APIKEY | Yes | 你的实际apikey (Your actual API key) |
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 |
|---|---|
| read_websiteA | Fast, token-efficient web content extraction - ideal for reading documentation, analyzing content, and gathering information from websites. Converts to clean Markdown while preserving links and 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 tool's purpose is clearly defined as web content extraction, making it impossible for an agent to misselect between non-existent alternatives.
The single tool name 'read_website' follows a clear verb_noun pattern, and with no other tools present, there is no inconsistency to evaluate. The naming is straightforward and predictable for this minimal set.
One tool is too few for a server named 'Read-Website', as it suggests a narrow scope that might limit functionality. While the tool is well-described, a single tool often feels insufficient for robust web content interactions, such as handling errors, managing sessions, or providing additional utilities like summarization or filtering.
The tool surface is severely incomplete for web content extraction. It lacks essential operations such as handling different content types (e.g., PDFs, images), managing cookies or authentication, retrying failed requests, or providing metadata extraction. This gap will likely cause agent failures in real-world scenarios beyond basic reading.