academic-research-mcp-server
Server Details
ArXiv preprints + Google Scholar papers, with citation counts in one query.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4/5 across 2 of 2 tools scored.
The two tools target distinct sources: arXiv focuses on specific disciplines (physics, math, CS), while Google Scholar covers all disciplines with citation counts. Their purposes are clearly differentiated, leaving no ambiguity.
Both tool names follow the identical pattern 'search_[source]' with snake_case, ensuring consistent and predictable naming.
With only 2 tools, the server feels minimal for an 'academic research' context. While search is core, additional tools (e.g., fetching full text, managing references) would be expected for completeness.
The tools cover searching two major databases but omit other essential academic research operations like retrieving detailed citations, managing bibliographies, or accessing full texts beyond search results. The surface is narrow.
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, setting a low burden. The description adds value by specifying the return fields (title, author list, abstract, publication date, PDF link, category classification), which goes beyond the annotations. However, it does not disclose behavioral traits like rate limits, authentication needs, or data freshness.
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 two sentences long, front-loaded with the action and scope, and each sentence provides essential information without redundancy. 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?
Given the tool is a straightforward search with good schema and annotations, the description covers the purpose, return fields, and usage suggestions. It lacks mention of pagination or sorting, but these are minor for a search tool. Output schema is absent, but the description partially compensates by listing return fields.
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?
Input schema has 100% coverage for both parameters (query and max_results). The description does not add new meaning beyond what is in the schema; it merely restates the concepts. Baseline is 3 due to high schema coverage.
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 clearly states the tool searches arXiv for academic papers, lists specific fields (physics, mathematics, computer science, related fields), and details the returned information (title, author list, abstract, publication date, PDF link, category classification). This distinguishes it from sibling search_google_scholar by focusing on a specific repository and field 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 provides usage context ('cutting-edge research, literature review, or staying current in academic fields') but does not explicitly state when not to use this tool or how it compares to the sibling tool. No exclusions or alternative recommendations are given.
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?
Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds return field details but does not disclose additional behavioral traits such as rate limits, pagination, or authentication needs. It does not contradict 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. The first sentence states the action and scope; the second lists returns and use cases. Information is front-loaded and efficiently organized.
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?
The description, combined with annotations and schema, provides sufficient context for a search tool. It lists return fields (title, authors, venue, citation count, abstract preview, link) but does not mention result ordering or pagination beyond the 'max_results' parameter. Still, it is largely complete for an agent to decide on usage.
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 100% coverage for both parameters (query and max_results) with adequate descriptions. The tool description does not add any extra semantic information beyond what the schema provides, so baseline 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?
The description clearly states 'Query Google Scholar' using a specific verb and resource, lists what it returns (paper title, authors, venue, etc.), and gives use cases. It implicitly distinguishes from sibling 'search_arxiv' by the resource name and 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 provides explicit usage scenarios: 'Use for comprehensive literature searches, citation tracking, or finding highly-cited works.' It does not explicitly mention when not to use or contrast with alternatives, but the context (Google Scholar vs arXiv) provides implicit guidance.
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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