Resume Optimizer
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| OPENAI_API_KEY | No | API key for OpenAI-compatible endpoints to let the Agent perform resume optimization independently. Optional; if not set, the host application's model performs the optimization. | |
| OPENAI_BASE_URL | No | Optional base URL for an OpenAI-compatible API endpoint. | |
| DEEPSEEK_API_KEY | No | API key for DeepSeek to let the Agent perform resume optimization independently. Optional; if not set, the host application's model performs the optimization. |
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
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| import_resumeA | 导入简历文件并转为 Markdown。支持 .md/.markdown/.txt/.docx/.pdf。返回 {markdown, sourceFormat, warnings}。Import a resume file (markdown/PDF/Word) and convert it to markdown text. |
| analyze_resumeA | 分析简历结构与完整度: 分区识别、完整度评分(0-100)、缺失/偏弱项、需要用户补充真实信息的问题清单、占位符检测; 可选传入目标岗位 JD 做关键词匹配。Analyze resume completeness and JD keyword match. |
| optimize_resumeA | 润色优化简历(不编造事实)。配置了 DEEPSEEK_API_KEY 或 OPENAI_API_KEY 时直接调用 LLM 返回润色结果; 否则返回 mode=host-llm 与完整润色指令, 由宿主应用中的模型按指令执行。Polish a resume honestly (STAR, quantification, JD alignment). Never fabricates employers or numbers. |
| export_resumeB | 把 Markdown 简历导出为 md / pdf / docx 文件。PDF 内置中文字体自动探测(PingFang/Noto/雅黑等)。Export markdown resume to md/pdf/docx file at out_path. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 4 tools
Each tool maps to a distinct pipeline stage: import (convert to markdown), analyze (structure/completeness), optimize (polish), export (write file). There is no overlap in purpose and the input/output types are clearly separated, so an agent can select unambiguously.
All four tools follow a strict verb_noun pattern (import_resume, analyze_resume, optimize_resume, export_resume) with a single shared noun. The convention is perfectly predictable.
Four tools is a tight, well-scoped set that maps cleanly onto a linear resume workflow. It is slightly on the lean side, but every tool earns its place with no redundancy.
The surface covers the full import → analyze → optimize → export lifecycle with no dead ends, which is strong for the stated purpose. Minor gaps exist (e.g. no versioning/template management or re-import of an optimized doc), but these are peripheral to the core workflow.