A
licenseNot graded
qualityD
maintenanceEnables reviewing and managing AI research paper candidates from arXiv, Semantic Scholar, and Hugging Face Daily Papers, scored against personal interests, via MCP tools in Claude Desktop or Cowork.
9
MIT
paperpal
by jerpint
This server provides access to academic paper databases, allowing you to:
Search for papers on Hugging Face using semantic search
Retrieve detailed information about papers from arXiv by their IDs
Enables searching and accessing research papers from arXiv's database to aid in literature reviews and academic research
Provides access to research papers hosted on Hugging Face, allowing users to discover and discuss AI/ML research
Mentioned as a planned integration for future development, likely to support academic writing workflows
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@paperpalfind recent papers about large language model alignment"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
🚨 Development has moved to https://github.com/milatechtransfer/paperpal
paperpal
MCP Extension to aid you in searching and writing literature reviews
Check out this conversation with Claude to see what it can do
How it works
paperpal gives your LLMs access to arxiv and Hugging Face papers.
You can then have a natural conversation with your favourite LLMs (e.g. Claude) and have it guide you.
You can:
Discuss papers
Look for new papers
Organize ideas for liteature reviews
etc.
Of course, this tool is as good as the sum of its parts. LLMs can still hallucinate, and semantic search is never perfect.
Related MCP server: arxivsub-mcp
Quickstart
There are many different ways with which you can interact with an MCP server.
Claude Desktop App
If this is your first time using an MCP server for Claude Desktop App, see https://modelcontextprotocol.io/quickstart/user
First, clone this repository locally:
git clone https://github.com/jerpint/paperpalNext, add the extension to your app. Open your configuration file (on macOS this should be ~/Library/Application Support/Claude/claude_desktop_config.json) and and add the following to the extension:
For example on MacOS:
{
"mcpServers": {
"paperpal": {
"command": "uv",
"args": [
"--directory",
"/Users/<username>/paperpal",
"run",
"paperpal.py"
]
}
}
}Restart your Claude Desktop App and you should see it appear.
Cursor
If this is your first time using an MCP server for Cursor, see https://docs.cursor.com/context/model-context-protocol#remote-development
First, clone this repository locally:
git clone https://github.com/jerpint/paperpalAdd this to the root of the project in a .cursor/mcp.json file:
{
"mcpServers": {
"paperpal": {
"command": "/Users/jeremypinto/.cargo/bin/uv",
"args": [
"--directory",
"/Users/jeremypinto/paperpal",
"run",
"paperpal.py"
]
}
}
}Available Tools
2 toolsfetch_paper_details_from_arxivB
Get the Arxiv info for a list of papers.
Args:
arxiv_ids (list[str] | str): The IDs of the papers to get the Arxiv info for, e.g. ["2503.01469", "2503.01470"]
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| arxiv_ids | Yes |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
B3.1/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool fetches info but doesn't describe key behaviors like whether it's a read-only operation, error handling for invalid IDs, rate limits, or authentication needs. This is a significant gap for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with the main purpose stated first and parameter details following. It uses two sentences efficiently, with no wasted words, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter) and the presence of an output schema, the description is somewhat complete but has gaps. It covers the basic purpose and parameter usage but lacks behavioral details and usage guidelines. With an output schema, it doesn't need to explain return values, but overall it's only minimally adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful semantics beyond the input schema. The schema has 0% description coverage, but the description explains that 'arxiv_ids' can be a list or string and provides an example (e.g., ["2503.01469", "2503.01470"]), clarifying usage. With only one parameter, this compensates well for the low schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get the Arxiv info for a list of papers.' It specifies the verb ('Get') and resource ('Arxiv info'), making it understandable. However, it doesn't explicitly differentiate from the sibling tool 'semantic_search_papers_on_huggingface', which appears to be a search tool rather than a direct fetch by ID, so it misses full sibling distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention the sibling tool or any other context for usage, such as prerequisites or scenarios where this tool is preferred over others. This leaves the agent without explicit direction on tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
semantic_search_papers_on_huggingfaceB
Search for papers on HuggingFace using semantic search.
Args:
query (str): The query term to search for. It will automatically determine if it should use keywords or a natural language query, so format your queries accordingly.
top_n (int): The number of papers to return. Default is 10, but you can set it to any number.
Returns:
str: A list of papers with the title, summary, ID, and upvotes.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| top_n | No |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
B3.4/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that the query 'will automatically determine if it should use keywords or a natural language query,' which adds some context about the tool's behavior. However, it lacks details on rate limits, authentication needs, error handling, or what happens with invalid inputs, which are important for a search tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with the core purpose stated first. The Args and Returns sections are structured clearly, though the 'Returns' section could be more concise (e.g., listing fields without full sentences). Overall, it's efficient with minimal waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, no annotations, but with an output schema), the description is reasonably complete. It explains the parameters and return format, and the output schema likely covers the return structure in detail. However, it could benefit from more behavioral context (e.g., search scope, limitations) to be fully comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds significant meaning beyond the input schema, which has 0% description coverage. It explains that 'query' can be keywords or natural language and will be automatically interpreted, and it specifies the default and flexibility for 'top_n'. This compensates well for the schema's lack of descriptions, though it doesn't cover all possible edge cases (e.g., query length limits).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search for papers on HuggingFace using semantic search.' This specifies the verb (search), resource (papers on HuggingFace), and method (semantic search). However, it doesn't explicitly differentiate from the sibling tool 'fetch_paper_details_from_arxiv' (which appears to fetch details rather than search), so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. While it mentions semantic search, it doesn't explain when to prefer this over keyword-based search or the sibling tool. There's no mention of prerequisites, limitations, or typical use cases, leaving the agent with minimal context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
B3.3/5.0
Disambiguation5/5
The two tools have clearly distinct purposes: one fetches details for specific Arxiv papers by ID, while the other performs semantic searches on HuggingFace. There is no overlap in functionality or ambiguity between them.
Naming Consistency5/5
Both tools follow a consistent snake_case naming pattern with descriptive verb_noun structures (fetch_paper_details_from_arxiv and semantic_search_papers_on_huggingface). The naming is predictable and readable throughout.
Tool Count2/5
With only two tools, the server feels under-scoped for a paper-related domain. It lacks basic operations like listing papers, filtering, or managing collections, which limits its utility for comprehensive paper handling tasks.
Completeness2/5
The toolset is severely incomplete for paper management. It covers fetching details and searching, but misses essential CRUD operations (e.g., saving, organizing, or annotating papers) and lacks integration between the two sources, leaving significant gaps in workflow coverage.
Maintenance
ActivityInactive
ResponsivenessNo issues
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