Academic Research MCP Server
Server Details
MCP server for academic research data including scholarly papers, citations, research trends, and publication metadata for AI agents.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.5/5 across 2 of 2 tools scored.
Each tool targets a distinct source and use case: search_arxiv for arXiv preprint searching in specific fields, search_google_scholar for comprehensive multi-disciplinary searches with citation tracking. Descriptions clearly differentiate their purposes.
Both tool names follow a consistent 'search_<source>' pattern (search_arxiv, search_google_scholar), making them predictable and easy to distinguish.
With only 2 search tools, the server feels too thin for the broad 'Academic Research' scope. It lacks essential tools for retrieving full papers, managing references, or exploring other academic resources.
The tool surface is severely incomplete for academic research. Only search functionality is provided, missing critical operations like fetching paper details, citation analysis, author search, or reference management.
Available Tools
2 toolssearch_arxivARead-onlyInspect
Search 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) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint and openWorldHint. Description adds value by detailing return fields: title, author list, abstract, publication date, PDF link, and category classification. No contradiction.
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 sentences: first states purpose, second lists return fields and usage. Efficient, front-loaded, 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 search tool with 2 parameters and no output schema, description adequately covers purpose, return fields, and usage. Uses annotations for behavioral traits.
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 'query' and 'max_results'. Description adds no extra parameter meaning beyond schema, so baseline score of 3 is appropriate.
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 specific verb 'Search', resource 'arXiv preprint repository', and domain 'physics, mathematics, computer science, and related fields'. Clearly distinguishes from sibling 'search_google_scholar' by focusing on academic preprints.
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 clear usage contexts: 'cutting-edge research, literature review, or staying current'. Does not explicitly state when not to use or differentiate from sibling, but context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_google_scholarARead-onlyInspect
Query 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) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnlyHint, openWorldHint), the description reveals return fields including 'full-text link if available', which adds practical context. No contradiction with 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 sentences with no wasted words. First sentence establishes action and scope. Second sentence mentions returns and use cases. Efficient and well-structured.
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 params, no output schema), the description adequately covers what the tool returns and when to use it. No gaps identified.
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?
Parameters are fully described in the schema (100% coverage). The description adds value by recommending 'max_results' for focused results. While it restates parameters, it provides additional usage guidance.
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, citations, and research articles, and lists return fields. It effectively distinguishes from sibling tool search_arxiv by implying a broader scope.
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 explicitly recommends use for literature searches, citation tracking, and finding highly-cited works. While it doesn't explicitly mention when not to use it, the use cases are clear and helpful for an agent.
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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