一个公开 Unpaywall 工具的 MCP (Model Context Protocol) 服务器，以便 AI 客户端可以：
通过 DOI 获取元数据
搜索文章标题
检索最佳 OA 全文链接
下载并从 OA PDF 中提取文本
将其添加到您的 MCP 客户端配置中（以 Claude Desktop 为例）：
{
"mcpServers": {
"unpaywall": {
"command": "npx",
"args": ["-y", "unpaywall-mcp"],
"env": { "UNPAYWALL_EMAIL": "you@example.com" }
}
}
}然后尝试使用这些工具：unpaywall_search_titles、unpaywall_get_fulltext_links、unpaywall_fetch_pdf_text。
您无需克隆此仓库或运行 npm install — npx 会在首次调用时处理获取和缓存。
Related MCP server: ScholarScope MCP
Node.js 18+ (用于 npx)
用于 Unpaywall / OpenAlex 请求的电子邮件地址（Unpaywall 要求，用于 OpenAlex 的礼貌池）。
最终用户应使用上述 npx 配置。从源码构建的贡献者请使用：
npm install
npm run build
UNPAYWALL_EMAIL=you@example.com npm start # stdio transport, as required by MCP clients热运行（无构建步骤）：
UNPAYWALL_EMAIL=you@example.com npm run dev描述：获取 DOI 的 Unpaywall 元数据
输入模式：
doi (字符串，必填)：例如 10.1038/nphys1170
email (字符串，可选)：如果提供，将覆盖 UNPAYWALL_EMAIL
输出：来自 Unpaywall 的 JSON 响应
描述：搜索文章标题并为每个命中结果返回 Unpaywall 风格的 OA 元数据（每页 50 个结果）
输入模式：
query (字符串，必填)：标题查询
is_oa (布尔值，可选)：如果为 true，则仅返回 OA 结果；如果为 false，则仅返回闭源结果；省略则返回全部
page (整数 >= 1，可选)：页码
email (字符串，可选)：覆盖 UNPAYWALL_EMAIL
输出：匹配 Unpaywall 搜索格式的 JSON — results[].response 是一个 DOI 风格的记录（doi、title、is_oa、oa_status、best_oa_location、oa_locations），每个结果包含 score 和 snippet。_source: "openalex" 标记了上游来源。
注意：由 OpenAlex 的 /works 端点支持，因为 Unpaywall 自己的 /v2/search 自 2025 年 5 月重写以来一直返回 HTTP 500。Unpaywall 现在作为 OpenAlex 的子程序运行，因此这是规范的现代等效方案。
描述：返回 DOI 的最佳 OA PDF URL 和 Open URL，以及所有 OA 位置
输入模式：
doi (字符串，必填)
email (字符串，可选)：覆盖 UNPAYWALL_EMAIL
输出：包含以下字段的 JSON：best_pdf_url、best_open_url、best_oa_location、oa_locations 以及部分元数据
描述：下载并从 DOI 的最佳 OA PDF 中提取文本，或从提供的 pdf_url 中提取
输入模式：
pdf_url (字符串，可选)：直接 PDF URL（优先级最高）
doi (字符串，可选)：如果未提供 pdf_url，则用于解析最佳 OA PDF
email (字符串，可选)：如果使用 doi 且没有 UNPAYWALL_EMAIL 环境变量，则为必填项
truncate_chars (整数 >= 1000，可选)：返回提取文本的最大字符数（默认 20000）
输出：包含 text（可能被截断）、length_chars、truncated、pdf_url 和 PDF 元数据的 JSON
当从支持 MCP 的 LLM 客户端使用此服务器时，请要求模型：
先搜索后获取：使用 unpaywall_search_titles 和简洁的标题短语；选择一个结果；然后对选定的 DOI 调用 unpaywall_get_fulltext_links 或 unpaywall_fetch_pdf_text。
优先选择 OA：当您只需要开放获取内容时，在搜索中传递 is_oa: true。
控制大小：在 unpaywall_fetch_pdf_text 中设置 truncate_chars（默认 20000），并在继续之前总结长文本。
保持弹性：如果缺少最佳 PDF URL，请回退到 best_open_url 并从落地页提取内容（在此服务器之外）。
遵守速率限制：如果进行大量调用，请间隔请求；重复使用之前的响应，而不是重复相同的调用。
给 LLM 的良好用户指令：
“查找 3 篇关于‘生物医学中的基础模型’的 OA 论文，然后提取并总结最好的一篇的引言。”
“搜索‘Graph Neural Networks survey 2024’，如果可能请过滤为 OA，然后获取 PDF 文本并生成 10 点摘要。”
根据您的 MCP 客户端，结构会有所不同；核心负载为：
// Search titles
{
"name": "unpaywall_search_titles",
"arguments": {
"query": "graph neural networks survey",
"is_oa": true,
"page": 1
}
}// Get best OA links for a DOI
{
"name": "unpaywall_get_fulltext_links",
"arguments": {
"doi": "10.48550/arXiv.1812.08434"
}
}// Fetch and extract PDF text (by DOI)
{
"name": "unpaywall_fetch_pdf_text",
"arguments": {
"doi": "10.48550/arXiv.1812.08434",
"truncate_chars": 20000
}
}推荐（无需构建）的 Claude Desktop 配置，使用 npm/npx：
{
"mcpServers": {
"unpaywall": {
"command": "npx",
"args": ["-y", "unpaywall-mcp"],
"env": {
"UNPAYWALL_EMAIL": "you@example.com"
}
}
}
}替代（本地仓库）配置，使用编译后的 dist：
{
"mcpServers": {
"unpaywall": {
"command": "node",
"args": ["/absolute/path/to/dist/index.js"],
"env": {
"UNPAYWALL_EMAIL": "you@example.com"
}
}
}
}添加后，请让您的客户端列出工具并尝试：
使用 query 调用 unpaywall_search_titles
使用 doi 调用 unpaywall_get_fulltext_links
使用 doi（或 pdf_url）调用 unpaywall_fetch_pdf_text
请遵守 Unpaywall 的速率限制和使用指南：https://unpaywall.org/products/api
服务器使用 stdio 传输和 @modelcontextprotocol/sdk。
设置 UNPAYWALL_EMAIL 或在每次调用时传递 email，以便 Unpaywall 可以就使用情况与您联系。
# 1) Build the project (also runs automatically on publish)
npm run build
# 2) Bump version (choose patch/minor/major)
npm version patch
# 3) Publish (ensure you are logged in: npm login)
npm publish --access public
# 4) Tag a release on GitHub (optional, recommended)用户随后可以按照上述说明使用 npx -y unpaywall-mcp 配置其 MCP 客户端。无需克隆或构建。
Download and extract text from best OA PDF for a DOI, or from a provided PDF URL.
| Name | Required | Description | Default |
|---|---|---|---|
| doi | No | DOI string or DOI URL. Used if pdf_url is not provided. | |
| pdf_url | No | Direct PDF URL to download and parse (takes precedence over DOI). | |
| No | Email to identify requests to Unpaywall (required when resolving via DOI). | ||
| truncate_chars | No | Max characters of extracted text to return (default 20000). |
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. It mentions key behaviors like downloading, extracting text, and precedence rules, but lacks details on error handling, rate limits, authentication needs (beyond the email parameter), or what 'best OA PDF' means. This provides basic context but is incomplete for a tool with potential external dependencies.
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 purpose ('Download and extract text') and includes essential qualifiers. Every word earns its place, with no wasted text, making it highly concise and well-structured.
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 and no output schema, the description provides basic purpose and parameter context but lacks completeness. It doesn't explain return values (e.g., text format, error responses) or behavioral details like rate limits, which are important for a tool interacting with external services. This is adequate but has clear gaps for effective agent 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 100%, so the schema fully documents all parameters. The description adds no additional semantic meaning beyond what's in the schema (e.g., it doesn't explain format details or usage nuances). This meets the baseline for high schema coverage but doesn't enhance parameter understanding.
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 ('Download and extract text') and the resource ('best OA PDF for a DOI, or from a provided PDF URL'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'unpaywall_get_fulltext_links' which might provide links rather than extracted text, so it misses full sibling distinction.
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 by mentioning alternatives ('DOI, or from a provided PDF URL') and precedence rules ('pdf_url takes precedence over DOI'), but it doesn't explicitly state when to use this tool vs. siblings like 'unpaywall_get_by_doi' or provide clear exclusions. This leaves some ambiguity in tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch Unpaywall metadata for a DOI (accepts DOI, DOI URL, or 'doi:' prefix). Requires an email address via env UNPAYWALL_EMAIL or the optional 'email' argument.
| Name | Required | Description | Default |
|---|---|---|---|
| doi | Yes | DOI string or DOI URL, e.g. 10.1038/nphys1170 or https://doi.org/10.1038/nphys1170 | |
| No | Email to identify your requests to Unpaywall (optional override) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses key behavioral traits: the tool requires an email address (via env or argument) for identification to Unpaywall, implying authentication needs. However, it lacks details on rate limits, error handling, or response format, leaving gaps in behavioral context.
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 two sentences that efficiently convey purpose, input formats, and requirements without any wasted words. Every sentence adds essential information, making it highly concise and well-structured.
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 output schema, no annotations), the description is adequate but incomplete. It covers authentication needs and input formats but lacks details on output (metadata structure), error cases, or performance constraints, which would be helpful 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?
Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds minimal value beyond the schema by mentioning the 'doi:' prefix as an accepted format and noting the email requirement, but does not provide additional syntax or usage details for parameters.
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 specific action ('Fetch Unpaywall metadata') and resource ('for a DOI'), distinguishing it from sibling tools like fetching PDF text or searching titles. It specifies the exact input formats accepted (DOI, DOI URL, or 'doi:' prefix), making the purpose 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 clear context on when to use this tool (for fetching metadata by DOI) and mentions an alternative (using the 'email' argument vs. env variable), but does not explicitly state when not to use it or differentiate from siblings like 'unpaywall_get_fulltext_links' beyond the metadata focus.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Given a DOI, return best open-access links (best PDF URL and open URL) plus Unpaywall locations metadata.
| Name | Required | Description | Default |
|---|---|---|---|
| doi | Yes | DOI string or DOI URL | |
| No | Email to identify your requests to Unpaywall (optional override) |
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. It describes what the tool returns (links and metadata) but does not mention rate limits, authentication needs (though 'email' is optional), error handling, or data freshness. It adds basic context but lacks details on operational traits like performance or constraints.
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 functionality ('return best open-access links') and includes key details (PDF URL, open URL, metadata) without waste. Every word earns its place, making it highly concise and well-structured.
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 output schema, no annotations), the description is reasonably complete. It specifies the input (DOI) and output (links and metadata), but without an output schema, it could benefit from more detail on return values (e.g., structure or examples). It covers the essentials but has minor gaps in output clarification.
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 ('doi' as required string and 'email' as optional override). The description adds no additional meaning beyond what the schema provides, such as format examples or usage tips for parameters. Baseline 3 is appropriate when schema does the heavy lifting.
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 specific action ('return best open-access links') and resource ('given a DOI'), distinguishing it from siblings like 'unpaywall_fetch_pdf_text' (which extracts text) and 'unpaywall_search_titles' (which searches titles). It explicitly mentions the output includes 'best PDF URL and open URL' plus metadata, making the purpose highly specific and differentiated.
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 context by specifying 'given a DOI' for retrieving open-access links, but it does not explicitly state when to use this tool versus alternatives like 'unpaywall_get_by_doi' (which might return different data) or 'unpaywall_fetch_pdf_text' (which focuses on text extraction). It provides clear context but lacks explicit exclusions or named alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search Unpaywall for article titles matching a query. Supports optional is_oa filter and pagination (50 results per page).
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Title search query (supports phrase, boolean operators per Unpaywall docs) | |
| is_oa | No | If true, only return OA results; if false, only closed; omit for all | |
| page | No | Page number (50 results per page) | |
| No | Email to identify your requests to Unpaywall (optional override) |
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. It mentions 'Supports optional is_oa filter and pagination (50 results per page)', which provides useful operational context about filtering capabilities and pagination behavior. However, it doesn't address important behavioral aspects like rate limits, authentication requirements (beyond the optional email parameter), error conditions, or what the search results actually contain.
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 (two sentences) and front-loaded with the core purpose. Every sentence earns its place: the first establishes what the tool does, and the second provides key operational context about filters and pagination. There's zero 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?
For a search tool with 4 parameters, 100% schema coverage, and no output schema, the description provides adequate but minimal context. It covers the basic purpose and mentions key operational features (filtering, pagination), but doesn't explain what the search results contain, how comprehensive the search is, or any limitations. Given the lack of annotations and output schema, more completeness would be helpful 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?
With 100% schema description coverage, the input schema already fully documents all 4 parameters. The description adds minimal value beyond what's in the schema - it mentions the optional is_oa filter and pagination (50 results per page), but these are already clearly documented in the parameter descriptions. The baseline score of 3 is appropriate when the schema does the heavy lifting.
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 Unpaywall for article titles matching a query') and specifies the resource ('article titles'), making the purpose immediately understandable. However, it doesn't explicitly differentiate this tool from its siblings (unpaywall_fetch_pdf_text, unpaywall_get_by_doi, unpaywall_get_fulltext_links), which appear to have different functions but could potentially overlap in some search contexts.
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 context through 'Search Unpaywall for article titles matching a query' and mentions optional filters/pagination, but doesn't explicitly state when to use this tool versus alternatives like unpaywall_get_by_doi (for DOI-based lookups) or unpaywall_get_fulltext_links (for link retrieval). No explicit when-not-to-use guidance or prerequisite information is provided.
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.
unpaywall_fetch_pdf_textunpaywall_get_by_doiunpaywall_get_fulltext_linksunpaywall_search_titlesScored across 4 tools
Each tool has a clearly distinct purpose: fetching PDF text, retrieving metadata, getting fulltext links, and searching titles. There is no overlap in functionality, making it easy for an agent to select the right tool for a specific task.
All tools follow a consistent 'unpaywall_verb_noun' pattern, such as unpaywall_fetch_pdf_text and unpaywall_search_titles. This uniformity enhances readability and predictability across the toolset.
With 4 tools, the server is well-scoped for handling Unpaywall-related operations, covering key actions like metadata retrieval, text extraction, link fetching, and title search without being overly sparse or bloated.
The toolset covers essential CRUD-like operations for accessing open-access content, including fetching, searching, and retrieving links. A minor gap might be the lack of update or delete tools, but this is reasonable given the server's read-only nature for external data.
Unpaywall MCP — open-access lookup for scholarly papers (free, no key)
Academic literature search, retrieval, and private library management on top of OpenAlex.
Find research papers and arXiv preprints, and get an abstract and free full text from a DOI or id.
Scrape arXiv, OpenAlex and Crossref papers by author, topic, journal or DOI. Pay per row.
