[News & Media] Search Google News for articles matching a query. Returns headlines, sources, URLs, and publication dates.
Args: query: Search query (e.g. "artificial intelligence", "climate change") max_results: Maximum number of results (default 10, max 100)
| 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 focuses on what results are returned. It adds context about the returned fields and parameter defaults, which goes beyond the annotations without contradicting them.
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 concise: three sentences covering purpose, return fields, and arguments. It uses a clear structure with 'Args:' section and front-loads the action. Every sentence is essential.
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 low complexity (2 parameters, no output schema, simple read-only operation), the description covers purpose, inputs, and outputs comprehensively. The inclusion of example queries and default values ensures an agent can invoke the tool correctly without ambiguity.
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 input schema has 0% coverage (no descriptions for properties), but the description compensates fully by documenting both parameters: query with examples (e.g., 'artificial intelligence') and max_results with default and max values. This provides complete semantic context.
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 the tool 'Search Google News for articles matching a query' and lists the return fields (headlines, sources, URLs, publication dates). It distinguishes itself from sibling tools like news__get_ap_news which are source-specific, making its purpose unambiguous.
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 explains what the tool does but lacks explicit guidance on when to use it versus alternatives (e.g., 'use when you need a broad news search from multiple sources'). The name and description imply it's for Google News, but no direct when/why advice is given.
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.
Many tools have overlapping functionality across categories (e.g., multiple search_arxiv, search_google_scholar, real estate tools, DNS/WHOIS checks). An agent would struggle to differentiate between similar tools from different categories, leading to ambiguity.
Tools follow a 'category__verb_noun' pattern mostly, but verbs vary (get, search, screen, check, etc.) and some categories use different orders (e.g., 'get_repo_stats' vs 'search_repos'). The consistency is acceptable but not uniform across the entire set.
With 152 tools, the server is excessively large for a single MCP server. While it aims to be an all-in-one gateway, the sheer number overwhelms the agent and likely exceeds practical limits for coherent selection.
The server covers a wide range of domains (finance, real estate, news, developer tools, etc.) but has notable gaps (e.g., social media APIs, CRM tools). Coverage is broad but not exhaustive, and some niche areas (e.g., global stock exchanges) are over-represented.