library-mcp是一个用于与 Markdown 知识库交互的 MCP 服务器。它包含一个文件夹，该文件夹可能有或没有子文件夹，包含以.md为扩展名的文件，并以如下元数据开头：
----
title: My blog post
tags:
- python
- programming
url: /my-blog-post
---
# My blog post
Yesterday I was dreaming about...当前版本的典型工作流程是检索给定标签的最新内容，然后讨论使用该标签：
Get the next 50 posts with tag "executive",
then tell me what I should do about this problem
I am running into: ...您也可以按日期范围执行相同操作：
Summarize the blog posts I wrote in the past year.您可能会合理地问“为什么不直接将整个博客上传到上下文窗口？”并且该库在两个方面优于这种方法：
我的博客语料库比目前大多数模型的上下文窗口要大得多。此外，即使上下文窗口变得非常大，我过去也写了很多平庸的东西，所以也许省略它也是一个特点。
我有许多不同的 Markdown 知识库，这使我可以同时操作它们。
最后，这是一个业余项目，旨在在您的笔记本电脑上本地运行。目前为止，还没有人因使用该软件而受到伤害，但它确实运行良好！
该 MCP 服务器公开了这些工具。
用于将内容检索到上下文窗口中的工具：
get_by_tag - 通过标签检索内容
get_by_text - 搜索内容中的特定文本
get_by_slug_or_url - 通过 slug 或 URL 查找帖子
get_by_date_range - 获取在某个日期范围内发布的帖子
浏览知识库的工具：
search_tags - 搜索与查询匹配的标签
list_all_tags - 列出按帖子数和新近度排序的所有标签
处理运行该工具的工具：
rebuild - 重建内容索引，如果您添加了更多内容、编辑了现有内容等，则很有用
这些说明描述了在 OS X 上安装Claude Desktop的方法。它在其他平台上应该可以类似地工作。
安装Claude Desktop 。
将library-mcp克隆到一个方便的位置，我假设是/Users/will/library-mcp
确保你已经安装了uv ，你可以按照这些说明进行操作
前往 Cladue Desktop，然后依次选择“设置”、“开发者”，并让其创建你的 MCP 配置文件。之后，你需要更新claude_desktop_config.json文件。（注意，你应该将will替换为你的用户，例如whoami的输出。）
cd /Users/will/Library/Application Support/Claude
vi claude_desktop_config.json然后添加此部分：
{
"mcpServers": {
"library": {
"command": "uv",
"args": [
"--directory",
"/Users/will/library-mcp",
"run",
"main.py",
"/Users/will/irrational_hugo/content"
]
}
}
}关闭 Claude 并重新打开它。
它应该可以工作...
Get posts published within a date range.
Args: start_date: the start date in ISO format (YYYY-MM-DD) end_date: the end date in ISO format (YYYY-MM-DD) limit: the maximum number of posts to return
| Name | Required | Description | Default |
|---|---|---|---|
| start_date | Yes | ||
| end_date | Yes | ||
| limit | No |
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 mentions the tool 'Get[s] posts' but doesn't specify whether this is a read-only operation, requires authentication, has rate limits, or describes the return format (e.g., pagination, error handling). For a tool with zero annotation coverage, this leaves significant behavioral gaps.
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 appropriately sized and front-loaded, with the purpose stated clearly in the first sentence. The parameter explanations are concise and directly relevant, with no wasted words. However, the structure could be slightly improved by integrating parameter details more seamlessly rather than as a separate 'Args:' section.
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 moderate complexity (3 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and parameter semantics but lacks behavioral details (e.g., safety, performance) and output information. Without annotations or an output schema, it should do more to be fully complete for an AI agent.
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 meaningful semantics beyond the input schema, which has 0% description coverage. It explains that 'start_date' and 'end_date' are in ISO format (YYYY-MM-DD) and defines 'limit' as 'the maximum number of posts to return,' clarifying purpose and format. This compensates well for the low schema coverage, though it doesn't cover all potential nuances like date inclusivity.
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's purpose: 'Get posts published within a date range.' This specifies the verb ('Get'), resource ('posts'), and scope ('within a date range'). However, it doesn't explicitly differentiate from sibling tools like 'get_by_tag' or 'search_tags', which might also retrieve posts but with different filters.
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 sibling tools like 'get_by_tag' or 'search_tags' for filtering by other criteria, nor does it specify prerequisites or exclusions. Usage is implied by the date-range focus but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get a post by its slug or URL.
Args: identifier: the slug, URL, or path fragment to search for
| Name | Required | Description | Default |
|---|---|---|---|
| identifier | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the tool retrieves a post but doesn't disclose behavioral traits like whether it returns a single result or multiple matches, error handling for invalid identifiers, authentication needs, or rate limits. This is a significant gap for a retrieval tool with zero annotation coverage.
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 appropriately sized with two sentences: a clear purpose statement and a parameter explanation. It's front-loaded with the core functionality, and the Args section adds necessary detail without redundancy. There's minimal waste, though the structure could be slightly more polished.
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 retrieval operation with no output schema and no annotations), the description is incomplete. It lacks details on return values, error cases, and behavioral constraints, making it inadequate for an agent to fully understand how to invoke and interpret results. More context is needed for a tool with zero structured coverage.
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 meaning by explaining that the 'identifier' parameter can be a slug, URL, or path fragment, which clarifies its purpose beyond the schema's generic 'string' type. However, with 0% schema description coverage and only 1 parameter, this provides basic but not comprehensive semantic context, meeting the baseline for minimal parameter info.
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 verb ('Get') and resource ('a post'), specifying it retrieves content by slug or URL. It distinguishes from siblings like get_by_date_range or get_by_tag by focusing on identifier-based lookup rather than date, tag, or text criteria. However, it doesn't explicitly contrast with all siblings (e.g., search_tags).
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 implies usage when you have a slug, URL, or path fragment to find a specific post, suggesting it's for precise retrieval rather than filtering or listing. It doesn't explicitly state when NOT to use it or name alternatives like get_by_text for content-based searches, leaving some ambiguity versus other lookup tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get blog content by its tag.
Args: tag: the tag associated with content limit: the number of results to include
| Name | Required | Description | Default |
|---|---|---|---|
| tag | Yes | ||
| limit | No |
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 'Get[s] blog content' but doesn't clarify what 'blog content' includes (e.g., full posts, summaries, metadata), whether results are paginated or sorted, or if there are rate limits or authentication requirements. For a read operation with zero annotation coverage, this leaves significant gaps in understanding the tool's behavior.
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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by a brief 'Args' section. There's no wasted text, though the structure could be slightly improved by integrating parameter details more seamlessly rather than a separate list.
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 2 parameters, 0% schema coverage, no annotations, and no output schema, the description is incomplete. It doesn't explain what 'blog content' entails as output, how results are formatted, or error conditions. For a tool with multiple siblings and no structured support, more context is needed to guide effective 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 adds basic semantics for both parameters: 'tag' is 'the tag associated with content' and 'limit' is 'the number of results to include'. This clarifies purpose but lacks details like tag format (e.g., case sensitivity) or limit constraints (e.g., max value). With 2 parameters and low schema coverage, this provides marginal value beyond the schema's structure.
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's purpose: 'Get blog content by its tag' specifies the verb ('Get') and resource ('blog content'), and distinguishes it from siblings like 'get_by_date_range' or 'get_by_slug_or_url' by focusing on tag-based retrieval. However, it doesn't explicitly differentiate from 'search_tags' or 'list_all_tags', which are related but distinct 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 'get_by_tag' over 'get_by_text' for content retrieval, nor does it specify prerequisites or exclusions. The agent must infer usage from the tool name and sibling names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get blog content by text in content.
Args: query: text for an exact match limit: the number of results to include
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| limit | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'exact match' for the query parameter, which hints at behavior, but doesn't disclose critical traits like whether this is a read-only operation, potential rate limits, authentication needs, error conditions, or what happens when no matches are found. For a retrieval tool with no annotation coverage, this leaves significant behavioral gaps.
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 appropriately sized and front-loaded with the core purpose in the first sentence. The Args section is clear and directly relevant. There's no unnecessary fluff, though the structure could be slightly improved by integrating the Args details more seamlessly rather than as a separate block.
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 2 parameters with 0% schema coverage, no annotations, and no output schema, the description is incomplete. It covers basic parameter purposes but lacks details on return values (e.g., format of blog content), error handling, or performance considerations. For a tool with siblings offering similar functionality, more context is needed to guide effective 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 adds meaning by specifying that 'query' is for 'text for an exact match' and 'limit' is for 'the number of results to include', which clarifies basic semantics beyond the schema's titles. However, it doesn't explain format constraints (e.g., query length, limit bounds) or provide examples, leaving some ambiguity.
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 'Get blog content by text in content' which provides a basic purpose (retrieving blog content using text matching), but it's vague about the matching mechanism ('exact match' is only mentioned in the Args section). It doesn't distinguish this tool from siblings like 'search_tags' or 'get_by_tag' which might also involve text-based retrieval. The purpose is understandable but lacks specificity about what makes this tool unique.
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. With siblings like 'get_by_date_range', 'get_by_slug_or_url', 'get_by_tag', and 'search_tags', there's no indication of when text-based content retrieval is preferred over other filtering methods. The Args section mentions 'exact match' but doesn't clarify use cases or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all tags sorted by number of posts and most recent post.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses the sorting behavior, which is useful, but doesn't mention other traits like pagination, rate limits, permissions needed, or what 'posts' refers to. It's adequate but has clear gaps for a read operation.
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 front-loads the core action and includes essential sorting details. Every word earns its place with no waste or redundancy.
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, no output schema, and 0 parameters, the description is minimally complete. It covers the purpose and sorting, but lacks context on return format, error handling, or system-specific details. It's adequate for a simple list tool but could be more informative.
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?
There are 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description doesn't add param info, which is fine here. Baseline for 0 params is 4, as it appropriately omits unnecessary details.
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 verb 'List' and resource 'all tags', specifying the sorting criteria 'by number of posts and most recent post'. It distinguishes from siblings like 'get_by_tag' (which likely filters) and 'search_tags' (which likely searches). However, it doesn't explicitly contrast with all siblings, preventing a perfect score.
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 implies usage when needing all tags with sorting, but doesn't explicitly state when to use this vs. alternatives like 'search_tags' or 'get_by_tag'. It provides some context through the sorting detail, but lacks explicit guidance on exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Rebuild text index. Useful for when contents have changed on disk
| 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 full burden for behavioral disclosure. It mentions the tool's purpose but doesn't describe what 'rebuild' entails operationally - whether it's destructive, how long it takes, whether it requires specific permissions, or what side effects occur. The phrase 'contents have changed on disk' hints at a maintenance operation but lacks crucial behavioral details like whether existing data is preserved or overwritten during the rebuild.
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 perfectly concise - two short sentences that each earn their place. The first sentence states the core action, the second provides essential usage context. No wasted words, no redundancy, and the most important information (what the tool does) comes first. This is an excellent example of efficient documentation.
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 that this is a maintenance/rebuild operation with no annotations and no output schema, the description should provide more complete context about what happens during execution. While it states the purpose and when to use it, it doesn't describe what 'rebuild' means operationally, what the expected outcome is, or any performance/behavioral characteristics. For a tool that likely modifies system state, this leaves significant gaps in understanding.
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 tool has zero parameters with 100% schema description coverage, so the schema already fully documents the parameter situation (none). The description appropriately doesn't discuss parameters since none exist. It focuses instead on the tool's purpose and usage context, which is the right approach for a parameterless tool. A baseline of 4 is appropriate when the schema handles all parameter documentation needs.
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 ('Rebuild text index') and the resource affected ('text index'), making the purpose immediately understandable. It distinguishes this tool from its siblings (which are all query/search tools) by being a maintenance/update operation rather than a data retrieval function. However, it doesn't specify what exactly gets rebuilt or where the index is located, keeping it from a perfect score.
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 explicit guidance on when to use this tool: 'Useful for when contents have changed on disk.' This gives clear context about the triggering condition. However, it doesn't specify when NOT to use it or mention alternatives (though none are obvious among the sibling tools, which are all query operations). The guidance is helpful but could be more comprehensive.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for tags matching the provided query.
Args: tag_query: partial or full tag name to search for limit: the maximum number of tags to return
| Name | Required | Description | Default |
|---|---|---|---|
| tag_query | Yes | ||
| limit | No |
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 searches for tags but doesn't describe behavioral traits such as permissions needed, rate limits, pagination, or what happens if no matches are found. This is inadequate for a search tool with zero annotation coverage.
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 appropriately sized and front-loaded, with the core purpose stated first followed by concise parameter explanations. Every sentence adds value without redundancy, making it efficient and easy to parse.
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 moderate complexity (2 parameters, no annotations, no output schema), the description is minimally adequate. It covers the basic purpose and parameters but lacks details on behavior, output format, and sibling differentiation, leaving gaps for the agent to navigate.
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 meaningful semantics for both parameters: 'tag_query' is explained as 'partial or full tag name to search for,' and 'limit' as 'the maximum number of tags to return.' With schema description coverage at 0%, this compensates well by clarifying parameter purposes beyond the bare schema, though it lacks format or constraint details.
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's purpose as 'Search for tags matching the provided query,' which specifies the verb (search) and resource (tags). It distinguishes from siblings like 'list_all_tags' by indicating a search rather than listing all, but doesn't explicitly differentiate from 'get_by_tag' or other search-related 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 like 'get_by_tag' or 'list_all_tags.' It mentions the query parameter but doesn't specify use cases, exclusions, or prerequisites, 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.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
get_by_date_rangeget_by_slug_or_urlget_by_tagget_by_textlist_all_tagsrebuildsearch_tagsScored across 7 tools
Most tools have distinct purposes focused on different retrieval methods (by date, slug/URL, tag, text content) and tag operations, but 'get_by_slug_or_url' and 'get_by_text' could potentially overlap if users confuse slug/URL with text content queries. The descriptions help clarify, but there's minor ambiguity.
All tool names follow a consistent verb_noun pattern with underscores (e.g., get_by_date_range, list_all_tags, search_tags). The naming is predictable and readable throughout the set, with no deviations in style.
With 7 tools, this is well-scoped for a library/blog content server. It covers core retrieval operations and tag management without being overwhelming, and each tool serves a clear purpose in the domain.
The toolset provides good read/search coverage for blog content and tags, but lacks any create, update, or delete operations, which are notable gaps for a full CRUD lifecycle. Agents can retrieve and rebuild but not modify content, limiting workflow completeness.
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