[Academic Research] Search Google Scholar for academic papers. Returns titles, authors, citations, and links.
Args: query: Search query (e.g. 'deep learning medical imaging') max_results: Maximum papers to return (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 indicate readOnlyHint and openWorldHint. The description adds that it returns specific fields (titles, authors, citations, links) and accepts a query, but does not disclose additional behaviors like rate limits, pagination, or result ordering. The added value beyond annotations is moderate.
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 efficient: one sentence for purpose and a clear bullet list for parameters. Every sentence serves a purpose. No redundancy or filler.
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 search tool with two parameters and no output schema, the description adequately covers input semantics and return fields. It could mention if results are sorted by relevance or date, but it is sufficiently complete given the tool's simplicity.
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?
Schema coverage is 0%, meaning descriptions must compensate. The description provides an example for query ('deep learning medical imaging') and explains max_results as 'Maximum papers to return (default 10)'. This adds meaningful context beyond the schema's type-only definitions.
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 it searches Google Scholar for academic papers and specifies the return fields (titles, authors, citations, links). This distinguishes it from sibling tools like search_arxiv, which searches a different repository.
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 provides the tool's purpose but does not explicitly state when to use this tool over alternatives such as academic-research__search_arxiv or developer-tools__search_google_scholar. The context is implied for academic paper searches, but no exclusions or heuristics are 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.