mcp-server-collector
mcp-server-collector MCP 服务器
用于通过互联网收集 MCP 服务器的 MCP 服务器。
成分
资源
暂无资源。
提示
尚无提示。
工具
服务器实现了3个工具:
extract-mcp-servers-from-url:从给定的 URL 中提取 MCP 服务器。
将“url”作为必需的字符串参数
extract-mcp-servers-from-content:从给定内容中提取 MCP 服务器。
将“内容”作为必需的字符串参数
submit-mcp-server:将 MCP 服务器提交到 MCP 服务器目录,如 mcp.so。
将“url”作为必需字符串参数,将“avatar_url”作为可选字符串参数
Related MCP server: MCP Server Neurolorap
配置
需要设置.env 文件。
OPENAI_API_KEY="sk-xxx"
OPENAI_BASE_URL="https://api.openai.com/v1"
OPENAI_MODEL="gpt-4o-mini"
MCP_SERVER_SUBMIT_URL="https://mcp.so/api/submit-project"快速入门
安装
克劳德桌面
在 MacOS 上: ~/Library/Application\ Support/Claude/claude_desktop_config.json在 Windows 上: %APPDATA%/Claude/claude_desktop_config.json
发展
构建和发布
准备分发包:
同步依赖项并更新锁文件:
uv sync构建软件包分发版:
uv build这将在dist/目录中创建源和轮子分布。
发布到 PyPI:
uv publish注意:您需要通过环境变量或命令标志设置 PyPI 凭据:
令牌:
--token或UV_PUBLISH_TOKEN或用户名/密码:
--username/UV_PUBLISH_USERNAME和--password/UV_PUBLISH_PASSWORD
调试
由于 MCP 服务器通过 stdio 运行,调试起来可能比较困难。为了获得最佳调试体验,我们强烈建议使用MCP Inspector 。
您可以使用以下命令通过npm启动 MCP Inspector:
npx @modelcontextprotocol/inspector uv --directory path-to/mcp-server-collector run mcp-server-collector启动后,检查器将显示一个 URL,您可以在浏览器中访问该 URL 以开始调试。
社区
关于作者
Available Tools
3 toolsextract-mcp-servers-from-contentC
Extract MCP Servers from given content
| Name | Required | Description | Default |
|---|---|---|---|
| content | Yes | content containing mcp servers |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states what the tool does ('extract MCP servers') without explaining how it behaves: e.g., what format the extraction outputs, whether it's read-only or has side effects, error handling, or performance considerations. This is inadequate for a tool with no annotation coverage, as it leaves critical behavioral traits unspecified.
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 description is extremely concise with a single sentence: 'Extract MCP Servers from given content'. It is front-loaded and wastes no words, making it easy to parse. Every part of the sentence earns its place by stating the action and target, though it could benefit from more detail for clarity.
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 complexity of extraction tasks, lack of annotations, and no output schema, the description is incomplete. It doesn't explain what 'extract' entails (e.g., parsing, formatting, or validation), what the output looks like, or how it differs from sibling tools. For a tool with no structured support beyond the input schema, this leaves significant gaps in understanding its full context and usage.
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?
The description adds no meaning beyond what the input schema provides. The schema has 100% coverage with one parameter 'content' described as 'content containing mcp servers', which the description implicitly references but doesn't elaborate on. With high schema coverage, the baseline is 3, as the schema already documents the parameter adequately, and the description doesn't compensate with additional context like examples or constraints.
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 states the tool's purpose as extracting MCP servers from content, which is clear but vague. It specifies the verb 'extract' and resource 'MCP servers', but doesn't differentiate from sibling tools like 'extract-mcp-servers-from-url' or 'submit-mcp-server' beyond the input source. The purpose is understandable but lacks specificity about what constitutes 'extraction' versus other operations.
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 description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to prefer this tool over 'extract-mcp-servers-from-url' (e.g., for direct content vs. URL fetching) or 'submit-mcp-server' (e.g., for extraction vs. submission). There's no context on prerequisites, exclusions, or typical use cases, leaving the agent to infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
extract-mcp-servers-from-urlC
Extract MCP Servers from a URL
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states what the tool does but lacks behavioral details: no information on permissions needed, rate limits, error handling, output format, or whether it's read-only/destructive. 'Extract' suggests read-only, but this isn't explicitly confirmed.
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 description is a single, efficient sentence with zero waste. It's appropriately sized for a simple tool and front-loaded with the core action, making it easy to parse quickly.
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 no annotations, 0% schema coverage, and no output schema, the description is incomplete. It lacks details on behavior, parameters, and return values, which are essential for a tool with one parameter and potential complexity in URL processing and MCP server extraction.
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?
Schema description coverage is 0% with 1 parameter ('url'), and the description doesn't add any parameter semantics beyond the name. It doesn't explain what type of URL is expected (e.g., HTTP, file path), format constraints, or examples, leaving the parameter meaning unclear.
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 'Extract MCP Servers from a URL' clearly states the verb ('extract'), resource ('MCP Servers'), and source ('from a URL'). It distinguishes from sibling 'extract-mcp-servers-from-content' by specifying URL vs. content, but doesn't differentiate from 'submit-mcp-server' which has a different action.
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?
No explicit guidance on when to use this tool vs. alternatives. The description implies usage for URL-based extraction, but doesn't specify scenarios, prerequisites, or exclusions compared to siblings like 'submit-mcp-server' for submission operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
submit-mcp-serverC
Submit MCP Server to MCP Servers Directory like mcp.so
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL of the MCP Server to submit | |
| avatar_url | No | avatar URL of the MCP Server to submit |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool submits to a directory 'like mcp.so', implying a public listing or registration, but doesn't clarify permissions required, rate limits, whether the submission is reversible, or what happens on success/failure. This is inadequate for a tool that likely involves external API calls.
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 description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is appropriately sized and front-loaded, making it easy to parse quickly.
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 and output schema, the description is incomplete. It doesn't explain what the tool returns, error conditions, or behavioral details like authentication needs. For a submission tool with external dependencies, this leaves significant gaps in understanding how to use it effectively.
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?
Schema description coverage is 100%, so the schema already documents both parameters ('url' and 'avatar_url') with clear descriptions. The description adds no additional meaning about parameters beyond what the schema provides, such as format examples or constraints, meeting the baseline for high schema coverage.
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 action ('Submit') and resource ('MCP Server to MCP Servers Directory like mcp.so'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'extract-mcp-servers-from-content' or 'extract-mcp-servers-from-url', which appear to be extraction tools rather than submission tools.
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 description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context for submission, or how it differs from sibling tools, leaving the agent to infer usage based on tool names alone.
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 updates
- First observed
extract-mcp-servers-from-content - First observed
extract-mcp-servers-from-url - First observed
submit-mcp-server
TDQS
Scored across 3 tools
Each tool has a clearly distinct purpose with no overlap: extracting servers from content, extracting from a URL, and submitting to a directory. The descriptions make it unambiguous which tool to use for each scenario, preventing misselection.
All tool names follow a consistent verb_noun pattern with hyphens (e.g., extract-mcp-servers-from-content, extract-mcp-servers-from-url, submit-mcp-server). This predictable naming scheme enhances readability and usability for agents.
With 3 tools, the server is well-scoped for its purpose of collecting and submitting MCP servers. Each tool earns its place by covering distinct aspects of the workflow, avoiding bloat or thinness.
The tool set covers the core workflow of extraction (from content and URLs) and submission, with no obvious dead ends. A minor gap might be the lack of tools for managing or listing already submitted servers, but agents can work around this with the existing tools.
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