一个模型上下文协议 (MCP) 服务器，通过简单的 API 接口提供对国会图书馆主题词表 (LCSH) 的访问。
此 MCP 服务器允许像 Claude 这样的 AI 助手使用公共 suggest2 API 搜索国会图书馆主题词表 (LCSH)。它提供了一个简洁的接口，用于查询 LCSH 数据并处理来自 API 的各种响应格式。
Related MCP server: MeSH MCP
安装 cataloger mcp server 最简单的方法是直接从 PyPI 安装：
pip install cataloger-mcp-server如果您更喜欢从源码安装：
git clone https://github.com/kltng/cataloger-mcp-server.git
cd cataloger-mcp-server
pip install -e .安装 Claude Desktop（如果尚未安装），请访问 https://claude.ai/desktop
安装 cataloger mcp server，使用上述任一安装方法
打开 Claude Desktop 并导航至设置：
点击左下角的个人资料图片
从菜单中选择“Settings”（设置）
配置 MCP 服务器：
在设置面板中，点击“MCP Servers”
点击“Add Server”
填写以下详细信息：
Name: cataloger mcp search
Command: cataloger-mcp-server
点击“Save”
启用服务器：
切换 cataloger mcp search 服务器旁边的开关以启用它
Claude 现在将拥有访问 LCSH 搜索功能的能力
安装 Cherry Studio，请遵循 Cherry 文档中的说明：https://docs.cherry-ai.com/docs/en-us/advanced-basic/mcp/install
安装 cataloger mcp server，使用上述任一安装方法。
准备 Cherry Studio 的 MCP JSON 配置。使用“Import JSON”方法，并使用如下代码片段：
{
"mcpServers": {
"cataloger-mcp": {
"command": "uvx",
"args": ["cataloger-mcp-server"]
}
}
}您可以将键 "cataloger-mcp" 修改为您在 Cherry Studio 中喜欢的任何标签。
导入到 Cherry Studio：
打开 Cherry Studio 并转到 Settings → MCP（或 MCP Servers）。
点击 Add / Import from JSON（措辞可能因版本而略有不同）。
粘贴上面的 JSON 代码片段并确认添加服务器。
验证服务器：
确保新的 cataloger-mcp-server 条目出现在 MCP 列表中并已启用。
开始新的聊天并确认 Cherry Studio 可以调用 cataloger MCP 工具。
一旦服务器在 Claude Desktop 中设置并启用，您就可以要求 Claude 搜索国会图书馆主题词表。以下是一些示例提示词：
“你能帮我搜索国会图书馆主题词表中关于 'artificial intelligence' 的内容吗？”
“在 LCSH 中查找 'climate change' 并告诉我官方主题词。”
“与 'quantum computing' 相关的 LCSH 术语有哪些？”
Claude 将使用 MCP 服务器查询 LCSH 和 LCNAF 数据库并返回结果。
“查找 'Smith, John Adam' 的名称规范记录。”
“在 LCSH 中对 'environmental policy' 执行关键词搜索。”
MCP 工具集成：公开了 search_lcsh（用于主题词，默认左对齐搜索）、 search_lcsh_keyword（用于主题词，关键词搜索）和 search_name_authority（用于人名）工具，可供 AI 助手使用。
资源端点：在 lcsh://search/{query} 和 lcnaf://search/{query} 提供资源端点。
强大的错误处理：优雅地处理 API 错误、连接问题和意外的响应格式。
多种响应格式：支持 LCSH API 的字典（命中）和列表响应格式。
如果您在使用 MCP 服务器时遇到问题：
检查服务器状态：在 Claude Desktop 中，转到 Settings > MCP Servers，检查服务器是否已启用并正在运行。
重启服务器：关闭服务器开关后再重新打开。
检查控制台输出：如果手动运行服务器，请检查控制台输出是否有任何错误消息。
验证网络连接：确保您的计算机有活动的互联网连接以访问 LCSH API。
本项目采用 MIT 许可证授权 - 有关详细信息，请参阅 LICENSE 文件。
有关服务器实现、API 参考和测试信息的更详细文档，请参阅 references.md 文件。
Search Library of Congress Subject Headings (LCSH) using the public suggest2 API. Returns a dictionary with the top results.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses the API source ('public suggest2 API') and return type ('dictionary with the top results'), but lacks details on error handling, rate limits, authentication needs, or what 'top results' entails (e.g., ranking criteria, number of results). This leaves behavioral gaps for a search tool.
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 concise with two sentences, front-loading the main action and resource. It avoids unnecessary words, though it could be slightly more structured (e.g., separating API details from return values). Every sentence contributes meaning, making it efficient.
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 tool's complexity (a search operation with no annotations, 0% schema coverage, and no output schema), the description is incomplete. It omits parameter details, behavioral traits like error handling, and specifics on the return value (e.g., dictionary structure). This is inadequate for effective tool use.
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%, so the description must compensate. It does not mention the 'query' parameter at all, failing to explain its purpose, format, or constraints. The description adds no semantic value beyond what the bare schema provides, leaving the parameter undocumented.
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 ('Search Library of Congress Subject Headings') and the resource (LCSH), with the specific verb 'search' and target 'LCSH'. It distinguishes itself by mentioning the 'public suggest2 API', though there are no sibling tools for comparison. The purpose is specific and unambiguous.
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, prerequisites, or exclusions. It mentions the 'public suggest2 API', but does not explain its context or limitations. With no sibling tools, this is less critical, but still lacks usage context.
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.
v1.0.0search_lcshScored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'search_lcsh' has a clearly defined and distinct purpose.
The naming follows a consistent verb_noun pattern with 'search_lcsh'. With only one tool, there is no inconsistency to evaluate, and the pattern is clear and appropriate.
A single tool is too few for a server focused on LCSH, as it lacks basic operations like browsing, filtering, or retrieving detailed subject information. This minimal set limits functionality and agent workflows.
The server is severely incomplete for LCSH operations; it only offers search without supporting actions like get_subject_details, list_subjects_by_category, or related term lookups. This creates significant gaps for agent tasks.
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