mcp-personal
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| MCP_API_TOKEN | No | Optional bearer token for HTTP authentication. If set, all requests must include Authorization: Bearer <token>. |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
| logging | {} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| extensions | {
"io.modelcontextprotocol/ui": {}
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| pingA | 连接自检：返回 "pong"。客户端接入后先调用它确认链路通畅。 |
| echoA | 原样返回输入的文本，用于测试工具参数传递是否正常。 |
| paper_searchA | 按关键词在 arXiv 检索学术论文，返回结构化列表（标题/作者/年份/摘要/链接）。
|
| pdf_extractA | 下载 arXiv 论文 PDF 并解析正文，返回页数与正文预览（论文精读前快速浏览用）。
|
| dataset_infoA | 分析本地数据集（CSV/JSON），返回结构/行数/每列统计，供竞赛数据处理前快速摸底。
|
| scaffold_projectA | 创建一个新项目的目录骨架（README/.gitignore/入口文件），返回创建的文件清单。
|
| course_reportA | 生成一份课设报告 Markdown 骨架并保存到 reports/ 目录，返回保存路径与结构。
|
| project_treeA | 列出目录的文件树（只读），默认跳过 .venv/.git/pycache 等目录。
|
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| literature_review | 为指定主题生成文献综述的写作框架（引言/分类/演进/评价/空白/结论 六段式）。 - topic: 综述主题，如 "model context protocol" - language: 输出语言（中文 / English） - sections: 主题章节数量（4-8） |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| list_papers | 列出本地论文库目录下的 PDF 文件（含大小），供模型在写作/阅读时使用。 |
TDQS
Scored across 8 tools
The eight actual tools have distinct purposes: connectivity/test, arXiv search, PDF text preview, dataset stats, project scaffolding, report generation, and file-tree listing. ping and echo are both test helpers but differ clearly (link check vs parameter passthrough), so an agent is unlikely to select the wrong tool.
Naming is a mix of bare verbs (ping, echo), verb+object (scaffold_project), object+verb (pdf_extract), and noun compounds (paper_search, project_tree, course_report). All names are lowercase, but there is no consistent verb_noun or action-oriented convention.
Eight tools is comfortably within the ideal range for a personal MCP server, and each tool provides a genuinely different capability. None feel like padding, and the count is not overwhelming for an agent to choose from.
For the inferred domain of research/development personal utilities, the advertised workflows are largely covered: search then extract papers, inspect datasets, scaffold a project, generate a report, and view a file tree. Minor gaps exist (e.g., no dataset sample-row preview or paper metadata fetch by ID), but they are workable and not severe dead ends.