resume-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| LLM_MODEL | No | The model identifier to use (e.g., deepseek/deepseek-v4-pro). Defaults to DeepSeek if not provided. | |
| LLM_API_KEY | Yes | API key for the LLM provider. Required for the resume optimization/generation to work. | |
| LLM_BASE_URL | No | Base URL for the OpenAI-compatible API endpoint (default: https://api.deepseek.com). | |
| RESUME_ENV_FILE | No | Optional path to a .env file from which to load LLM configuration. |
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": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| optimize_resumeA | 根据目标岗位优化一份已有简历（PDF / .docx / .doc）。 参数： resume_file_path: 简历文件的本地绝对路径（PDF、.docx 或 .doc）。 job_position: 目标岗位，例如「高级前端工程师」。 requirements: 可选的额外要求（如「突出项目管理经验」）。 output_dir: 输出目录；留空则写到输入文件所在目录。 返回：优化说明、结构化 JSON，以及生成的 .docx / .pdf 绝对路径。 |
| generate_resumeA | 根据零散背景信息，从零生成一份针对目标岗位的简历。 参数： background: 用户背景信息（教育、工作/实习、项目、技能等自由文本）。 job_position: 目标岗位，例如「后端开发工程师」。 requirements: 可选的额外要求。 output_dir: 输出目录；留空则写到当前工作目录。 返回：生成说明、结构化 JSON，以及生成的 .docx / .pdf 绝对路径。 |
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 2 tools
The two tools have clearly distinct purposes: one optimizes an existing resume, the other generates a new one from scratch. There is no overlap or ambiguity in their scopes.
Both tool names follow the same verb_noun pattern (optimize_resume, generate_resume), making them predictable and consistent.
With only two tools, the server is on the low end of the typical range, but the tools cover the two primary resume workflows (create and optimize). The count is slightly under but reasonable for the focused purpose.
The tool set covers the full lifecycle from generating a resume from raw background to optimizing an existing one. There are no obvious missing operations for the stated domain.