Search Google and return organic results with titles, URLs, snippets.
Args: query: Search query (e.g. 'best python libraries 2026') max_results: Max results (default 10)
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| max_results | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint, so the description needn't emphasize non-destructiveness. It adds value by specifying the output structure (titles, URLs, snippets), but lacks details on pagination, quotas, or failure modes.
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 and front-loaded: one sentence states the tool's function, and a short Args block covers parameters. No wasted words.
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?
For a simple 2-parameter tool with no output schema, the description adequately explains what is returned. It lacks explicit error handling details or notes on result format, but the provided information is sufficient for basic invocation.
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?
Even though the input schema has no descriptions, the tool description fully explains both parameters: query (with an example) and max_results (with default value). This adds meaning beyond the schema's bare type declarations.
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?
Description states a specific verb (Search) and resource (Google), and clarifies it returns organic results with titles, URLs, and snippets. This clearly distinguishes it from sibling tools like crawl_website and wikipedia_lookup.
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?
The description makes the use case clear: perform a Google search and obtain organic results. It doesn't explicitly exclude alternatives or provide when-not-to-use guidance, but the purpose is distinct enough to infer appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct source: crawl_website for general sites, google_search for search results, and wikipedia_lookup for Wikipedia articles. There is no overlap or ambiguity in their purposes.
All tool names follow a consistent lowercase_underscore style, with clear action-oriented naming (crawl_website, google_search, wikipedia_lookup). The convention is uniform across the entire server.
With only 3 tools, the server is well-scoped for basic web retrieval tasks. Each tool fills a distinct role, and the count is within the ideal range for a focused MCP server.
The tool set covers core web scraping activities: crawling a site, searching the web, and retrieving a specific knowledge article. A minor gap is the lack of a standalone fetch tool for a single URL, but crawl_website with max_pages=1 can serve that purpose.