Which integrations are available for this server?

Enables searching, downloading, and reading arXiv papers with filter options for date ranges and categories, plus paper analysis capabilities through specialized prompts. Allows installation and distribution of the server package through PyPI, with version tracking and download statistics.

How do I use ArXiv MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@ArXiv MCP Server search for recent papers about large language models" 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.

ArXiv MCP Server

by wr-web

Overview Schema Related Servers Score Discussions

Python

Hybrid

Twitter Follow Python Version Tests License: MIT PyPI Downloads PyPI Version

ArXiv MCP Server

🔍 Enable AI assistants to search and access arXiv papers through a simple MCP interface.

The ArXiv MCP Server provides a bridge between AI assistants and arXiv's research repository through the Model Context Protocol (MCP). It allows AI models to search for papers and access their content in a programmatic way.

🤝 Contribute • 📝 Report Bug

✨ Core Features

🔎 Paper Search: Query arXiv papers with filters for date ranges and categories
📄 Paper Access: Download and read paper content
📋 Paper Listing: View all downloaded papers
🗃️ Local Storage: Papers are saved locally for faster access
📝 Prompts: A Set of Research Prompts

Related MCP server: ArXiv Paper MCP

🚀 Quick Start

Installing via Smithery

To install ArXiv Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install arxiv-mcp-server --client claude

Installing Manually

Install using uv:

uv tool install arxiv-mcp-server

For development:

# Clone and set up development environment
git clone https://github.com/blazickjp/arxiv-mcp-server.git
cd arxiv-mcp-server

# Create and activate virtual environment
uv venv
source .venv/bin/activate

# Install with test dependencies
uv pip install -e ".[test]"

🔌 MCP Integration

Add this configuration to your MCP client config file:

{
    "mcpServers": {
        "arxiv-mcp-server": {
            "command": "uv",
            "args": [
                "tool",
                "run",
                "arxiv-mcp-server",
                "--storage-path", "/path/to/paper/storage"
            ]
        }
    }
}

For Development:

{
    "mcpServers": {
        "arxiv-mcp-server": {
            "command": "uv",
            "args": [
                "--directory",
                "path/to/cloned/arxiv-mcp-server",
                "run",
                "arxiv-mcp-server",
                "--storage-path", "/path/to/paper/storage"
            ]
        }
    }
}

💡 Available Tools

The server provides four main tools:

1. Paper Search

Search for papers with optional filters:

result = await call_tool("search_papers", {
    "query": "transformer architecture",
    "max_results": 10,
    "date_from": "2023-01-01",
    "categories": ["cs.AI", "cs.LG"]
})

2. Paper Download

Download a paper by its arXiv ID:

result = await call_tool("download_paper", {
    "paper_id": "2401.12345"
})

3. List Papers

View all downloaded papers:

result = await call_tool("list_papers", {})

4. Read Paper

Access the content of a downloaded paper:

result = await call_tool("read_paper", {
    "paper_id": "2401.12345"
})

📝 Research Prompts

The server offers specialized prompts to help analyze academic papers:

Paper Analysis Prompt

A comprehensive workflow for analyzing academic papers that only requires a paper ID:

result = await call_prompt("deep-paper-analysis", {
    "paper_id": "2401.12345"
})

This prompt includes:

Detailed instructions for using available tools (list_papers, download_paper, read_paper, search_papers)
A systematic workflow for paper analysis
Comprehensive analysis structure covering:
- Executive summary
- Research context
- Methodology analysis
- Results evaluation
- Practical and theoretical implications
- Future research directions
- Broader impacts

⚙️ Configuration

Configure through environment variables:

Variable	Purpose	Default
`ARXIV_STORAGE_PATH`	Paper storage location	~/.arxiv-mcp-server/papers

🧪 Testing

Run the test suite:

python -m pytest

📄 License

Released under the MIT License. See the LICENSE file for details.

Made with ❤️ by the Pearl Labs Team

Available Tools

4 tools

download_paperC

Download a paper and create a resource for it

ParametersJSON Schema

Name	Required	Description	Default
`paper_id`	Yes	The arXiv ID of the paper to download
`check_status`	No	If true, only check conversion status without downloading

TDQS

C2.9/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It mentions downloading and creating a resource, but doesn't disclose behavioral traits like what format the download is in, where the resource is stored, whether it's a read/write operation, potential rate limits, or error handling. This is inadequate for a tool with mutation implications.

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 a single, efficient sentence that states the core action. It's appropriately sized and front-loaded with the main purpose. However, it could be slightly more structured by separating the download and resource creation aspects for clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations, no output schema, and a tool that involves downloading and resource creation (implying mutation), the description is incomplete. It lacks details on what the resource is, how it's created, return values, error cases, or dependencies. This is insufficient for safe and effective use by an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents both parameters ('paper_id' and 'check_status') with clear descriptions. The description adds no additional meaning beyond what the schema provides, such as explaining the relationship between downloading and checking status. Baseline 3 is appropriate when schema does the heavy lifting.

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 with the verb 'download' and resource 'paper', and mentions creating a resource. It distinguishes from siblings like 'list_papers' and 'search_papers' by focusing on downloading, but doesn't explicitly differentiate from 'read_paper' which might have overlapping functionality.

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 like 'read_paper' or 'search_papers'. It doesn't mention prerequisites, such as needing a valid arXiv ID, or when the 'check_status' parameter should be used. Usage context is implied but not explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_papersB

List all existing papers available as resources

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

B3.1/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden but only states the basic action without disclosing behavioral traits such as pagination, rate limits, or what 'available as resources' entails. It's minimal and leaves key operational details unclear.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's function without unnecessary words. It's appropriately sized and front-loaded, making it easy to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'papers' are, how they're listed, or the return format, leaving significant gaps for a tool that likely returns a list of resources.

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 tool has 0 parameters with 100% schema description coverage, so the schema fully documents the lack of inputs. The description adds no parameter information, which is acceptable here, but it doesn't compensate for any gaps since there are none.

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 verb ('List') and resource ('all existing papers'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'search_papers' or 'read_paper', which prevents a perfect 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?

No guidance is provided on when to use this tool versus alternatives like 'search_papers' or 'download_paper'. The description implies a broad listing without filtering, but it lacks explicit instructions or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

read_paperB

Read the full content of a stored paper in markdown format

ParametersJSON Schema

Name	Required	Description	Default
`paper_id`	Yes	The arXiv ID of the paper to read

TDQS

B3.1/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It states the tool reads content in markdown format, but lacks behavioral details such as error handling (e.g., if paper_id is invalid), performance (e.g., size limits), or side effects (e.g., caching). This leaves gaps for safe agent operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero waste. It is front-loaded with the core purpose and includes essential format details, making it appropriately sized for the tool's simplicity.

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, no output schema, no annotations), the description is minimally adequate. It covers the basic action and format, but lacks completeness for safe use (e.g., no error or behavioral context), which is a gap despite the simple schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with the parameter 'paper_id' documented as 'The arXiv ID of the paper to read'. The description adds no additional parameter semantics beyond this, so it meets the baseline for high schema coverage without compensating value.

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 action ('read the full content') and resource ('stored paper'), specifying the output format ('in markdown format'). It distinguishes from siblings like 'download_paper' (likely for file retrieval) and 'list_papers'/'search_papers' (for listing/searching), but does not explicitly name these alternatives.

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?

No guidance is provided on when to use this tool versus alternatives. The description implies it's for reading content, but does not specify prerequisites (e.g., paper must be stored), exclusions, or direct comparisons to siblings like 'download_paper' for raw files.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

search_papersC

Search for papers on arXiv with advanced filtering

ParametersJSON Schema

Name	Required	Description
`query`	Yes
`max_results`	No
`date_from`	No
`date_to`	No
`categories`	No
`save_to_file`	No	Optional file path to save results
`min_citations`	No	Minimum citation count filter

TDQS

C2.9/5.0

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 mentions 'advanced filtering' but doesn't explain what that entails, such as rate limits, authentication needs, or what happens with the 'save_to_file' parameter. For a search tool with 7 parameters and no annotations, this is a significant gap in transparency about how the tool behaves beyond basic functionality.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core action ('search for papers on arXiv') and adds a key feature ('with advanced filtering'). There is no wasted text, making it highly concise and well-structured for quick understanding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of 7 parameters, low schema coverage (29%), no annotations, and no output schema, the description is incomplete. It doesn't explain return values, error handling, or detailed behavioral traits, leaving significant gaps for the agent to navigate. This is inadequate for a tool with multiple filtering options and potential side effects like file saving.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is low at 29%, with only 'save_to_file' and 'min_citations' having descriptions. The description adds minimal value by hinting at 'advanced filtering' which relates to parameters like 'categories', 'date_from', etc., but doesn't provide specific meanings or usage examples. It compensates somewhat but not enough to fully bridge the coverage gap, resulting in a baseline score.

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 verb ('search') and resource ('papers on arXiv'), making the purpose evident. It distinguishes from siblings like 'download_paper', 'list_papers', and 'read_paper' by specifying searching with filtering, though it doesn't explicitly differentiate from 'list_papers' which might also list papers. This is specific but could be more precise about the advanced filtering aspect.

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 like 'list_papers' or 'download_paper'. It mentions 'advanced filtering' but doesn't specify contexts or exclusions, leaving the agent to infer usage based on the tool name alone. This lack of explicit alternatives or conditions reduces its helpfulness.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

4 tool updatesAug 15, 2025
- First observeddownload_paper
- First observedlist_papers
- First observedread_paper
- First observedsearch_papers

Install Server

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TDQS

B3.4/5.0

Disambiguation5/5

Each tool has a distinct and clear purpose: download_paper fetches new papers, list_papers shows existing resources, read_paper accesses stored content, and search_papers finds papers on arXiv. There is no overlap in functionality, making tool selection straightforward for an agent.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern with snake_case (e.g., download_paper, list_papers, read_paper, search_papers). This uniformity enhances readability and predictability across the tool set.

Tool Count4/5

With 4 tools, the count is appropriate for a focused arXiv server, covering core operations like search, download, list, and read. It's slightly lean but reasonable; adding tools for metadata management or updates could improve coverage without being excessive.

Completeness4/5

The tool set covers essential arXiv workflows: searching, downloading, listing, and reading papers. Minor gaps exist, such as lacking tools for updating or deleting stored papers, but agents can still perform key tasks effectively within the domain.

Maintenance

ActivityInactive

ResponsivenessSyncing

Resources

GitHub Repository

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