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resume-analyzer-mcp

Name: resume-analyzer-mcp
Author: M-Sohaib-Siddiqui

by M-Sohaib-Siddiqui

Overview Schema Source Related Servers Discussions Admin

Python

Remote

Resume Analyzer MCP Server 📄🎯

一个基于 Python（mcp SDK）构建的开源 Model Context Protocol (MCP) 服务器，允许 AI 模型（Claude、Antigravity、Cursor）分析简历、计算客观的 ATS（申请人跟踪系统）评分，并建议针对职位定制的简历改进方案。

🌟 功能特性

🛠️ 工具

analyze_resume(resume_text: str, target_job_title: str)
- 评估结构完整性（摘要、经历、教育、技能、项目）。
- 检测高影响力的动作动词和量化绩效指标（例如 %, $, multipliers）。
- 识别与职位匹配的关键技能。
suggest_improvements(resume_text: str, target_job_title: str)
- 生成可操作、针对职位定制的要点改进建议。
- 识别薄弱/被动措辞，并推荐高影响力动词升级。
- 应用 Google 'XYZ 公式'（通过做 Z 完成 X，以 Y 衡量）。
calculate_ats_score(resume_text: str, job_description: str)
- 将简历文本与目标职位描述进行比较。
- 计算精确的 ATS 匹配百分比评分（例如 ATS 评分：82%）。
- 生成关键词匹配率、匹配术语和关键缺失关键词。

📚 资源

resume://sample-template：行业标准 Markdown 简历模板。
rules://ats-guidelines：ATS 解析标准和要点评分标准。

💬 提示词

full_resume_review：用于运行端到端职业审查工作流的预构建提示词模板。

Related MCP server: resume-tailor-mcp

💬 面向用户和客户的示例提示词

1. 简历结构分析（`analyze_resume`）

"你能使用 analyze_resume 审查我下面这份针对后端工程师职位的简历吗？"
# John Smith
Skills: Python, PostgreSQL, Docker, REST APIs
Experience: Software Engineer at Acme Inc. Developed backend APIs in Python and reduced database latency by 30%.
Education: B.S. in Computer Science

2. ATS 匹配评分（`calculate_ats_score`）

"请运行 calculate_ats_score 检查我针对这份职位描述的匹配百分比。"
简历： Skills: Python, SQL, PostgreSQL, Docker
职位描述： Seeking Senior Backend Engineer proficient in Python, SQL, PostgreSQL, Redis, Docker, Kubernetes, CI/CD.

3. 可操作的简历建议（`suggest_improvements`）

"你能针对数据工程师职位对我的简历运行 suggest_improvements 吗？"
Worked on data pipelines. Helped with SQL queries and handled daily reports.

4. 获取资源

"你能读取 resume://sample-template 资源，向我展示标准简历格式吗？"

🚀 快速开始与本地设置

1. 前提条件

Python 3.10 或更高版本
Git

2. 安装

克隆或导航到项目工作区：

cd D:\dev\resume-analyzer-mcp

# Create and activate virtual environment
python -m venv .venv
# On Windows PowerShell:
.\.venv\Scripts\Activate.ps1
# On macOS/Linux:
source .venv/bin/activate

# Install dependencies
pip install -e .

🧪 运行本地验证测试

运行自动化集成测试套件，该套件通过 stdio 以编程方式启动 MCP 服务器，并验证工具调用、资源和提示词：

python test_client.py

🔌 连接到 MCP 客户端

Claude Desktop 配置

将服务器配置添加到你的 claude_desktop_config.json：

Windows：%APPDATA%\Claude\claude_desktop_config.json
macOS：~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "resume-analyzer": {
      "command": "D:\\dev\\resume-analyzer-mcp\\.venv\\Scripts\\python.exe",
      "args": [
        "D:\\dev\\resume-analyzer-mcp\\server.py"
      ]
    }
  }
}

Antigravity / Gemini 配置

添加到你的工作区 .agents/mcp_config.json 或 ~/.gemini/config/mcp_config.json：

{
  "mcpServers": {
    "resume-analyzer": {
      "command": "python",
      "args": ["D:/dev/resume-analyzer-mcp/server.py"]
    }
  }
}

☁️ 部署到 Smithery MCP 市场

Smithery 允许开发者自动发现、托管和安装 MCP 服务器。

第 1 步：将仓库推送到 GitHub

git add .
git commit -m "Add example prompts and documentation"
git remote add origin https://github.com/your-username/resume-analyzer-mcp.git
git push -u origin main

第 2 步：发布到 Smithery

访问 smithery.ai 并使用 GitHub 登录。
点击 提交服务器 并选择你的 resume-analyzer-mcp 仓库。
Smithery 会自动从 smithery.yaml 和 Dockerfile 构建你的容器，并渲染你的 README.md 及交互式示例提示词！

📄 许可证

MIT 许可证。免费用于商业和非商业用途。

Available Tools

3 tools

analyze_resumeB

Analyzes the structure, completeness, impact metrics, action verbs, and skills of a resume.

ParametersJSON Schema

Name	Required	Description	Default
`resume_text`	Yes
`target_job_title`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

B3.2/5.0

Behavior3/5

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

No annotations are provided, so the description must carry the burden. It states the tool analyzes multiple qualitative and quantitative aspects, implying it is a read-only analytical operation. However, it doesn't disclose any side effects, dependencies, or the format of the output, leaving some behavioral ambiguity.

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 succinct—a single sentence that front-loads the core purpose. It is appropriately concise and avoids redundant wording.

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 moderate complexity, the description covers key analysis dimensions but lacks details on output and optional parameter usage. An output schema exists, so return format details are less critical, but the incomplete parameter guidance lowers the score.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate for parameter meaning. It doesn't elaborate on resume_text format or clarify the role of target_job_title even though it's optional. This is a significant gap for a tool with only two parameters.

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 identifies the action (analyzes) and the resource (resume), and lists several specific aspects: structure, completeness, impact metrics, action verbs, and skills. This distinguishes it well from siblings like suggest_improvements and calculate_ats_score, though it doesn't explicitly differentiate them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when a resume analysis is needed, listing key analysis dimensions. However, it doesn't explicitly state when to use this tool versus alternatives, such as when to use calculate_ats_score for scoring or suggest_improvements for changes.

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

calculate_ats_scoreA

Calculates ATS match percentage score (0-100%) by comparing resume text against a job description.

ParametersJSON Schema

Name	Required	Description	Default
`resume_text`	Yes
`job_description`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A4.3/5.0

Behavior4/5

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

With no annotations provided, the description carries the transparency burden. It discloses the output range (0-100%), the input types (resume text and job description), and the comparison action. This makes the tool's behavior clear, though it does not detail any underlying algorithm or edge cases, which is acceptable for a simple calculation tool.

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 sentence, front-loaded with the main purpose, and contains no fluff. Every word adds value, making it appropriately concise and well-structured.

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

Completeness5/5

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

For a simple tool with two string inputs and a numeric output, the description is complete. It explains the core functionality, and since an output schema exists, return values need not be elaborated. The tool's simplicity and clear purpose mean no additional behavioral context is required.

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 input schema has 0% description coverage, so the description must add meaning. It does this by explicitly stating that resume text is compared against a job description, clarifying the role of each parameter. Although parameter names are self-explanatory, the description reinforces their relationship and intended use.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool calculates an ATS match percentage score (0-100%) by comparing resume text against a job description. This specific verb and resource set it apart from sibling tools like analyze_resume and suggest_improvements, which likely focus on other aspects.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage contexts: use when you need a numeric ATS match score. However, it does not explicitly mention when to use this tool over siblings or when not to use it. The guidance is implied rather than explicit.

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

suggest_improvementsB

Generates actionable recommendations to optimize bullet points, action verbs, and formatting.

ParametersJSON Schema

Name	Required	Description	Default
`resume_text`	Yes
`target_job_title`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

B3.1/5.0

Behavior3/5

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

With no annotations provided, the description carries the full burden of disclosing behavior. It accurately states the tool generates recommendations without side effects, but it does not elaborate on the nature of the output, whether it reads or writes data, or any limitations. This is adequate for a simple suggestion tool but lacks depth, missing an opportunity to clarify that it is non-destructive.

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, well-structured sentence that front-loads the main action and clearly states the domain. Every word adds value, and it is appropriately sized for the tool's simplicity, achieving maximum conciseness.

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 (2 parameters, 1 optional) and the presence of an output schema (which likely documents return values), the description is mostly sufficient but leaves out any mention of the target_job_title parameter. It does not explain when to use this tool, but the core purpose is clear. It barely meets the minimum viable standard for completeness.

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

Parameters2/5

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

Schema coverage is 0%, so the description must compensate. It adds meaningful detail about resume_text (referencing bullet points, action verbs, formatting), but completely ignores the optional target_job_title parameter. This leaves a significant gap in understanding how the second parameter behaves, earning a below-average 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 uses a specific verb ('Generates') and resource ('actionable recommendations to optimize bullet points, action verbs, and formatting'), making the purpose unambiguous. However, it does not explicitly differentiate from sibling tools like analyze_resume or calculate_ats_score, though the focus on improvement recommendations is distinct enough to infer a difference.

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 offers no guidance on when to use this tool versus its siblings. There is no mention of alternatives, prerequisites, or typical scenarios. The only hint is the verb 'Generates', which implies a recommendation role, but no explicit usage context is provided.

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.

3 tool updatesv0.1.0Aug 20, 2026
- First observedanalyze_resume
- First observedcalculate_ats_score
- First observedsuggest_improvements

Deploy Server

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.4/5.0

Scored 2026-08-20 10:10 across 3 tools

Disambiguation4/5

The three tools have distinct purposes: analyzing resume quality, suggesting improvements, and calculating ATS match. However, 'analyze_resume' and 'suggest_improvements' could overlap slightly since both deal with resume content quality, though their outputs differ (analysis vs. recommendations).

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern: analyze_resume, suggest_improvements, calculate_ats_score. The pattern is predictable and clear.

Tool Count3/5

Three tools is on the leaner side for a resume analyzer, potentially leaving users wanting more functions like resume parsing or formatting help. However, the count is still within a reasonable range for a focused tool that handles core resume analysis tasks.

Completeness3/5

The core workflow covers analyzing a resume, suggesting improvements, and calculating ATS score. However, there's a notable gap: no tool to update or create a resume, and the tools likely rely on passing full resume text each time, making the workflow feel incomplete.

Maintenance

ActivitySlowing

ResponsivenessNo issues

Resources

GitHub Repository

Need Help?

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Resume Analyzer MCP Server 📄🎯

🌟 功能特性

🛠️ 工具

📚 资源

💬 提示词

💬 面向用户和客户的示例提示词

1. 简历结构分析（analyze_resume）

2. ATS 匹配评分（calculate_ats_score）

3. 可操作的简历建议（suggest_improvements）

4. 获取资源

🚀 快速开始与本地设置

1. 前提条件

2. 安装

🧪 运行本地验证测试

🔌 连接到 MCP 客户端

Claude Desktop 配置

Antigravity / Gemini 配置

☁️ 部署到 Smithery MCP 市场

第 1 步：将仓库推送到 GitHub

第 2 步：发布到 Smithery

📄 许可证

Available Tools

Output Schema

Output Schema

Output Schema

Tool Schema Changelog

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

1. 简历结构分析（`analyze_resume`）

2. ATS 匹配评分（`calculate_ats_score`）

3. 可操作的简历建议（`suggest_improvements`）