Web Search 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
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| search_webC | Search the web and return a list of result URLs and titles. Uses DuckDuckGo HTML. |
| fetch_pageC | Fetch a page and extract its readable content and title using Readability. |
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: fetch_page retrieves and extracts content from a specific URL, while search_web performs a general web search to find relevant URLs. There is no overlap or ambiguity between them, as one is for accessing known pages and the other is for discovering new ones.
Both tools follow a consistent verb_noun naming pattern (fetch_page and search_web), using clear, descriptive verbs that align with their functions. The naming is uniform and predictable across the set.
With only two tools, the server feels thin for a web search domain, as it lacks operations like advanced search filtering, result pagination, or handling different search engines. While the tools cover basic fetch and search, the count is borderline minimal for the apparent scope.
The tools provide core search and fetch capabilities, but there are notable gaps: no ability to refine searches (e.g., by date or site), manage search history, or handle errors like rate limits. This could lead to agent workarounds for more complex tasks.