websearch
Provides web search capabilities using DuckDuckGo as a fallback search engine. Allows performing web queries and retrieving search results.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@websearchsearch for latest AI breakthroughs"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
websearch_tool
Web search tool with two interfaces sharing one core. Serper primary, DuckDuckGo fallback (on error or empty results).
Install
uv pip install -e . # or: pip install -e .
export SERPER_API_KEY=<your-key>Related MCP server: Web Search MCP Server
HTTP API
uvicorn websearch.http_api:app --port 8000
curl "http://127.0.0.1:8000/search?q=anthropic+claude&num=5"Endpoint | Params | Response |
|
|
|
| — |
|
source is serper or ddg. Each result has title, url, snippet.
MCP Server
python -m websearch.mcp_server # stdio transportExposes one tool: web_search(query: str, num: int = 10).
MCP client config:
{
"mcpServers": {
"websearch": {
"command": "python",
"args": ["-m", "websearch.mcp_server"],
"env": { "SERPER_API_KEY": "<your-key>" }
}
}
}Library
from websearch import search
resp = search("anthropic claude", num=5) # reads SERPER_API_KEY from envAvailable Tools
1 toolweb_searchC
Search the web (Serper primary, DuckDuckGo fallback).
Args: query: The search query. num: Max number of results (1-50).
| Name | Required | Description | Default |
|---|---|---|---|
| num | No | ||
| query | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses the fallback mechanism (DuckDuckGo if Serper fails), which is a useful behavioral detail beyond the schema. However, it does not mention rate limits, query limits, idempotency, or that the tool is read-only. With no annotations, the description should provide more behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise, with two sentences that immediately convey the purpose and key parameters. There is no wasted text, and the structure is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of an output schema, the description should detail what is returned (e.g., result structure, fields). It also lacks information on error handling, rate limiting, or any prerequisites. For a simple web search tool, the description is inadequate for autonomous agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful semantics for the 'num' parameter by specifying the accepted range (1-50) and its purpose ('Max number of results'). For 'query', it simply repeats the schema's title. Since schema description coverage is 0%, the description compensates partially but not fully.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Search the web' with a specific verb and resource. It also mentions the backend providers (Serper primary, DuckDuckGo fallback), adding useful context. However, it could be more precise about the types of content retrieved (e.g., text, news, images).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives is provided. There are no sibling tools to differentiate, but the description lacks any context about appropriate use cases or situations where this tool might be unsuitable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
1 tool update
v0.1.0- First observed
web_search
TDQS
Only one tool exists, so there is no possibility of confusion between tools.
With a single tool named 'web_search', naming is trivially consistent.
One tool is minimal but acceptable for a focused search server; however, a more comprehensive set could include fetch or news tools.
The server only offers search functionality, lacking obvious features like fetching page content or news-specific search, which would be expected for a complete web search tool set.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
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Related MCP Servers
- FlicenseBqualityDmaintenanceAllows you to search the web using DuckDuckGo and optionally fetch and summarize content from search results.24-
- FlicenseNot gradedqualityCmaintenanceEnables web search across multiple search engines (DuckDuckGo, Bing, Startpage) with parallel execution and result deduplication. Also provides web page content extraction capabilities.2-
- AlicenseBqualityDmaintenanceEnables web search via Serper API with advanced search operators and webpage scraping capabilities to extract content in plain text or markdown format.23,022MIT
- FlicenseNot gradedqualityDmaintenanceProvides a Google search tool via the Serper API, returning structured results and a summary answer for LLMs like Claude.1-
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