MCP server for academic research data including scholarly papers, citations, research trends, and publication metadata for AI agents.
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Average 4.3/5 across 2 of 2 tools scored.
Both tools search for academic papers, but descriptions clearly differentiate: arXiv focuses on peer-reviewed papers in specific fields, while Google Scholar covers all disciplines with citation metrics. Users can distinguish by domain, but the core purpose is similar.
Both tools follow a consistent 'search_' prefix and underscore-separated names indicating the source, creating a predictable pattern.
With only two tools, the server feels minimal but not unreasonable for a focused search service. However, a broader research server might expect additional tools for fetching full text or managing bibliographies.
The server covers the core search functionality across two major databases, but lacks tools for retrieving papers by ID, downloading PDFs, or managing references, which are common complementary actions.
search_arxivASearch the arXiv preprint repository for peer-reviewed academic papers in physics, mathematics, computer science, and related fields. Returns paper title, author list, abstract, publication date, PDF link, and category classification. Use for cutting-edge research, literature review, or staying current in academic fields.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Research keywords or topic (e.g. 'neural networks', 'quantum computing', 'protein folding') | |
| max_results | No | Number of papers to return (default 10, higher values for comprehensive literature review) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint and openWorldHint, which the description does not contradict. The description adds useful context about returning metadata and peer-reviewed content. No mention of rate limits or pagination, but sufficient for a read-only search.
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?
Three sentences, efficient and front-loaded with the main action. No redundant information; every sentence adds value.
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 no output schema, the description adequately lists return fields. It covers the tool's domain and purpose. Could mention sorting or date range, but overall sufficient for a search tool.
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 100%, so parameters are already well-described. The description adds examples for 'query' and context for 'max_results' (default 10, use higher for comprehensive review), enhancing understanding beyond the schema.
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 searches arXiv for academic papers, lists returned fields (title, author, abstract, etc.), and specifies domains (physics, math, CS). It distinguishes itself from the sibling 'search_google_scholar' by focusing on arXiv.
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 suggests uses ('cutting-edge research, literature review, staying current') but does not explicitly state when to avoid this tool or compare with the sibling. It implies usage context but lacks exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_google_scholarAQuery Google Scholar for academic papers, citations, and research articles across all disciplines. Returns paper title, authors, publication venue, citation count, abstract preview, and full-text link if available. Use for comprehensive literature searches, citation tracking, or finding highly-cited works.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search terms or research topic (e.g. 'machine learning bias', 'climate change economics', 'gene therapy advances') | |
| max_results | No | Maximum papers to retrieve (default 10, recommended for focused results) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnly and openWorld. Description adds behavioral context (return fields, full-text link availability) beyond annotations.
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?
Two efficient sentences: first defines action and scope, second lists returns and use cases. 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 two-param tool with no output schema, description provides sufficient context about usage and return fields; missing pagination or sorting details but adequate.
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 100% with descriptions for both parameters; description adds no extra meaning beyond the schema.
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 queries Google Scholar for academic papers, lists specific return fields, and implicitly differentiates from sibling search_arxiv by source.
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?
Provides use cases like comprehensive literature searches and citation tracking, but does not explicitly exclude alternatives like search_arxiv for preprints.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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