|
| 1 | +{ |
| 2 | + "display_name": "ArXiv MCP Server", |
| 3 | + "license": "MIT", |
| 4 | + "tags": [ |
| 5 | + "arxiv", |
| 6 | + "research", |
| 7 | + "papers", |
| 8 | + "academic", |
| 9 | + "AI", |
| 10 | + "search", |
| 11 | + "MCP", |
| 12 | + "Model Context Protocol" |
| 13 | + ], |
| 14 | + "installations": { |
| 15 | + "uvx": { |
| 16 | + "type": "uvx", |
| 17 | + "command": "uv", |
| 18 | + "args": [ |
| 19 | + "tool", |
| 20 | + "run", |
| 21 | + "arxiv-mcp-server", |
| 22 | + "--storage-path", |
| 23 | + "/path/to/paper/storage" |
| 24 | + ], |
| 25 | + "description": "Run arxiv-mcp-server using uv tool" |
| 26 | + } |
| 27 | + }, |
| 28 | + "examples": [ |
| 29 | + { |
| 30 | + "title": "", |
| 31 | + "description": "", |
| 32 | + "prompt": "Search for recent papers on transformer architecture in AI and machine learning" |
| 33 | + }, |
| 34 | + { |
| 35 | + "title": "", |
| 36 | + "description": "", |
| 37 | + "prompt": "Find papers about reinforcement learning published after 2023" |
| 38 | + }, |
| 39 | + { |
| 40 | + "title": "", |
| 41 | + "description": "", |
| 42 | + "prompt": "Download the paper with ID 2401.12345" |
| 43 | + }, |
| 44 | + { |
| 45 | + "title": "", |
| 46 | + "description": "", |
| 47 | + "prompt": "Show me a list of all downloaded papers" |
| 48 | + }, |
| 49 | + { |
| 50 | + "title": "", |
| 51 | + "description": "", |
| 52 | + "prompt": "Read the content of paper 2401.12345" |
| 53 | + }, |
| 54 | + { |
| 55 | + "title": "", |
| 56 | + "description": "", |
| 57 | + "prompt": "Search for papers on quantum computing in the last 2 years" |
| 58 | + }, |
| 59 | + { |
| 60 | + "title": "", |
| 61 | + "description": "", |
| 62 | + "prompt": "Analyze paper 2401.12345 using the deep-paper-analysis prompt" |
| 63 | + }, |
| 64 | + { |
| 65 | + "title": "", |
| 66 | + "description": "", |
| 67 | + "prompt": "Find papers by Geoffrey Hinton about deep learning" |
| 68 | + }, |
| 69 | + { |
| 70 | + "title": "", |
| 71 | + "description": "", |
| 72 | + "prompt": "Search for papers on large language models in cs.AI and cs.CL categories" |
| 73 | + }, |
| 74 | + { |
| 75 | + "title": "", |
| 76 | + "description": "", |
| 77 | + "prompt": "Download and summarize the most recent paper on diffusion models" |
| 78 | + } |
| 79 | + ], |
| 80 | + "name": "arxiv-mcp", |
| 81 | + "repository": { |
| 82 | + "type": "git", |
| 83 | + "url": "https://github.com/blazickjp/arxiv-mcp-server" |
| 84 | + }, |
| 85 | + "homepage": "https://github.com/blazickjp/arxiv-mcp-server", |
| 86 | + "author": { |
| 87 | + "name": "blazickjp" |
| 88 | + }, |
| 89 | + "description": "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.", |
| 90 | + "categories": [ |
| 91 | + "Knowledge Base" |
| 92 | + ], |
| 93 | + "tools": [ |
| 94 | + { |
| 95 | + "name": "search_papers", |
| 96 | + "description": "Search for papers on arXiv with advanced filtering", |
| 97 | + "inputSchema": { |
| 98 | + "type": "object", |
| 99 | + "properties": { |
| 100 | + "query": { |
| 101 | + "type": "string" |
| 102 | + }, |
| 103 | + "max_results": { |
| 104 | + "type": "integer" |
| 105 | + }, |
| 106 | + "date_from": { |
| 107 | + "type": "string" |
| 108 | + }, |
| 109 | + "date_to": { |
| 110 | + "type": "string" |
| 111 | + }, |
| 112 | + "categories": { |
| 113 | + "type": "array", |
| 114 | + "items": { |
| 115 | + "type": "string" |
| 116 | + } |
| 117 | + } |
| 118 | + }, |
| 119 | + "required": [ |
| 120 | + "query" |
| 121 | + ] |
| 122 | + } |
| 123 | + }, |
| 124 | + { |
| 125 | + "name": "download_paper", |
| 126 | + "description": "Download a paper and create a resource for it", |
| 127 | + "inputSchema": { |
| 128 | + "type": "object", |
| 129 | + "properties": { |
| 130 | + "paper_id": { |
| 131 | + "type": "string", |
| 132 | + "description": "The arXiv ID of the paper to download" |
| 133 | + }, |
| 134 | + "check_status": { |
| 135 | + "type": "boolean", |
| 136 | + "description": "If true, only check conversion status without downloading", |
| 137 | + "default": false |
| 138 | + } |
| 139 | + }, |
| 140 | + "required": [ |
| 141 | + "paper_id" |
| 142 | + ] |
| 143 | + } |
| 144 | + }, |
| 145 | + { |
| 146 | + "name": "list_papers", |
| 147 | + "description": "List all existing papers available as resources", |
| 148 | + "inputSchema": { |
| 149 | + "type": "object", |
| 150 | + "properties": {}, |
| 151 | + "required": [] |
| 152 | + } |
| 153 | + }, |
| 154 | + { |
| 155 | + "name": "read_paper", |
| 156 | + "description": "Read the full content of a stored paper in markdown format", |
| 157 | + "inputSchema": { |
| 158 | + "type": "object", |
| 159 | + "properties": { |
| 160 | + "paper_id": { |
| 161 | + "type": "string", |
| 162 | + "description": "The arXiv ID of the paper to read" |
| 163 | + } |
| 164 | + }, |
| 165 | + "required": [ |
| 166 | + "paper_id" |
| 167 | + ] |
| 168 | + } |
| 169 | + } |
| 170 | + ], |
| 171 | + "prompts": [ |
| 172 | + { |
| 173 | + "name": "deep-paper-analysis", |
| 174 | + "description": "Analyze a specific paper in detail", |
| 175 | + "arguments": [ |
| 176 | + { |
| 177 | + "name": "paper_id", |
| 178 | + "description": "arXiv paper ID", |
| 179 | + "required": true |
| 180 | + } |
| 181 | + ] |
| 182 | + } |
| 183 | + ], |
| 184 | + "resources": [], |
| 185 | + "is_official": false |
| 186 | +} |
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