Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Rchilli Resume Parser MCP Server parse this resume URL and extract the candidate's work experience"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
用于访问 Rchilli Resume Parser1 API 的 MCP 服务器。
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🎯 进入 MCP 广场，浏览所有可用的 MCP 服务器
🔍 搜索或找到本服务器（bach-rchilli_resume_parser1）
🎉 点击 "安装 MCP" 按钮
✅ 完成！即可在您的应用中使用
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Related MCP server: Resume Filter MCP Server
这是一个 MCP 服务器，用于访问 Rchilli Resume Parser1 API。
PyPI 包名: bach-rchilli_resume_parser1
版本: 1.0.0
传输协议: stdio
pip install bach-rchilli_resume_parser1pip install -e .# 运行（uvx 会自动安装并运行）
uvx --from bach-rchilli_resume_parser1 bach_rchilli_resume_parser1
# 或指定版本
uvx --from bach-rchilli_resume_parser1@latest bach_rchilli_resume_parser1python server.py# 安装
pip install bach-rchilli_resume_parser1
# 运行（命令名使用下划线）
bach_rchilli_resume_parser1此 API 需要认证。请设置环境变量:
export API_KEY="your_api_key_here"变量名 | 说明 | 必需 |
| API 密钥 | 是 |
| 不适用 | 否 |
| 不适用 | 否 |
编辑 Cursor MCP 配置文件 ~/.cursor/mcp.json:
{
"mcpServers": {
"bach-rchilli_resume_parser1": {
"command": "uvx",
"args": ["--from", "bach-rchilli_resume_parser1", "bach_rchilli_resume_parser1"],
"env": {
"API_KEY": "your_api_key_here"
}
}
}
}编辑 Claude Desktop 配置文件 claude_desktop_config.json:
{
"mcpServers": {
"bach-rchilli_resume_parser1": {
"command": "uvx",
"args": ["--from", "bach-rchilli_resume_parser1", "bach_rchilli_resume_parser1"],
"env": {
"API_KEY": "your_api_key_here"
}
}
}
}此服务器提供以下工具:
resume_parser_url_apiResume Parser Url API allows you to parse resume using resume public URL.
端点: POST /parseResume
resume_parser_binary_apiResume Parser Binary API allows you to parse the resume using binary data in base64 format.
端点: POST /parseResumeBinary
传输协议: stdio
HTTP 客户端: httpx
MIT License - 详见 LICENSE 文件。
此服务器由 API-to-MCP 工具生成。
版本: 1.0.0
Resume Parser Binary API allows you to parse the resume using binary data in base64 format.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure but fails to mention output format (structured JSON, raw text?), side effects (logging, storage), idempotency, or error conditions. It only describes the input modality.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The single sentence wastes words restating the tool name concept ('Resume Parser Binary API allows you to') before getting to the action. It could be more direct (e.g., 'Parse a resume from base64-encoded binary data').
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations, output schema, and defined input parameters, the description is insufficient. It omits what the tool returns (parsed fields, confidence scores?), error handling, and whether the operation is read-only or creates resources.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Per the rubric, 0 parameters establishes a baseline of 4. The description adds critical context that base64-encoded binary data is expected as input, which the empty schema cannot convey. However, there is a concerning disconnect between the described base64 input and the empty parameter schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool parses resumes and specifies the input method (binary data in base64 format), distinguishing it from the sibling 'resume_parser_url_api'. However, it uses filler words ('allows you to') instead of a direct verb, slightly weakening clarity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The mention of 'binary data in base64 format' implies usage when file content is available locally, contrasting with the URL-based sibling. However, there is no explicit guidance on when to choose this tool versus the URL alternative or prerequisites like file size limits.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Resume Parser Url API allows you to parse resume using resume public URL.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Mentions 'public URL' (indicating the resume must be publicly accessible), but fails to disclose output format, supported file types (PDF, DOCX), error conditions, or privacy implications of sending URLs to the parser.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence structure is efficient, but contains filler words ('allows you to') and tautology (repeating 'Resume Parser Url API' from the name). The core value ('parse resume using resume public URL') is present but not front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Resume parsing is a moderately complex operation, yet the description provides no information about the return value or output structure, and there is no output schema to compensate. Given the lack of annotations, the description should explain what structured data is returned (e.g., contact info, work history).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema contains zero parameters. Per scoring rules, 0 params establishes a baseline of 4. The description mentions 'resume public URL' which aligns with the tool's implied parameter needs, though this is not formally documented in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States the specific action (parse) and resource (resume) clearly. Effectively distinguishes from sibling tool 'resume_parser_binary_api' by specifying 'public URL' as the input method, clarifying the scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies usage context by specifying 'public URL,' suggesting when to use this over the binary alternative (when a URL is available vs. a local file). However, lacks explicit when-not guidance or direct comparison to the sibling tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0resume_parser_binary_apiresume_parser_url_apiScored across 2 tools
The two tools are clearly distinct: one parses resumes from binary data in base64 format, and the other from a public URL. There is no overlap in their purposes or input methods, making it easy for an agent to choose the correct tool based on the data source available.
Both tools follow a consistent verb_noun pattern with 'resume_parser' as the prefix, followed by 'binary_api' and 'url_api' to specify the input type. This naming convention is predictable and enhances readability across the tool set.
With only two tools, the server feels thin for a resume parser domain. While it covers two common input methods (binary and URL), it lacks tools for other operations like retrieving parsed results, updating data, or handling errors, which limits its utility and scope.
The server only provides parsing tools without any complementary operations such as storing, retrieving, or managing parsed resumes. This creates significant gaps in the lifecycle of resume data, likely causing agent failures when more than basic parsing is needed.
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