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cite-mcp

by ndchikin
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Python
Remote

CiteAssist MCP 服务器

铁匠徽章

模型上下文协议 (MCP) 服务器，提供来自 CiteAs 和 Google Scholar 的 BibTeX 格式的引文数据。将引文检索功能直接集成到您的应用程序中，增强您的研究工作流程。

成分

工具

  • get_citeas_data - 从 CiteAs 检索指定资源的 BibTeX 格式的引用

    • resource （字符串，必需）：DOI、URL、关键字

  • get_scholar_data - 从 Google Scholar 检索 BibTeX 格式的引文

    • query （字符串，必需）：搜索查询

    • results （整数，可选）：结果数量（默认值：2）

Related MCP server: BibTeX MCP Server

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

开发/未发布的服务器配置：

"mcpServers": {
  "reference-mcp": {
    "command": "uv",
    "args": [
      "--directory",
      "/path/to/project/dir",
      "run",
      "reference-mcp"
    ]
  }
}

已发布的服务器配置：

"mcpServers": {
  "reference-mcp": {
    "command": "uvx",
    "args": [
      "reference-mcp"
    ]
  }
}

通过 Smithery 安装

要通过Smithery自动为 Claude Desktop 安装reference-mcp：

npx -y @smithery/cli install @ndchikin/reference-mcp --client claude

发展

构建和发布

准备分发包：

  1. 同步依赖项并更新锁文件：

uv sync

  1. 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。

  1. 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/project/dir run reference-mcp

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。

Available Tools

2 tools
get_citeas_dataC

Retrieve BibTeX-formatted citation for the specified resource (e.g., DOI, URL, keyword) from the CiteAs

ParametersJSON Schema
NameRequiredDescriptionDefault
resourceYes

TDQS

C2.9/5.0
Behavior2/5

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 states the tool retrieves data, implying a read-only operation, but doesn't cover important aspects like error handling, rate limits, authentication needs, or response format details beyond 'BibTeX-formatted'.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that clearly states the tool's purpose and parameter usage. It's appropriately sized for a simple tool with one parameter, though it could be slightly more structured with separate usage guidance.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

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 for a tool that presumably returns structured citation data. It doesn't explain what happens with invalid inputs, the format of returned BibTeX, or how this differs from the sibling tool's functionality.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds meaningful context by explaining that the 'resource' parameter can be a DOI, URL, or keyword, which goes beyond the schema's generic 'string' type. However, with 0% schema description coverage and only one parameter, this provides adequate but not comprehensive semantic information.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Retrieve' and the resource 'BibTeX-formatted citation', specifying it's from the CiteAs service. It distinguishes from the sibling tool 'get_scholar_data' by focusing on citation formatting rather than scholarly data, though the distinction could be more explicit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides minimal guidance by mentioning the resource parameter can be a DOI, URL, or keyword, but offers no explicit advice on when to use this tool versus the sibling 'get_scholar_data'. It lacks context on alternatives or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_scholar_dataC

Retrieve results BibTeX-formatted citations for publications matching the query from the Google Scholar

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYes
resultsNo

TDQS

C2.9/5.0
Behavior2/5

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 the retrieval action and output format but lacks details on rate limits, authentication needs, error handling, or what happens if no results are found. This is a significant gap for a tool that likely interacts with an external API.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with no wasted words. It front-loads the key action and resource, making it easy to scan and understand quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of querying an external service like Google Scholar, no annotations, no output schema, and low schema coverage, the description is incomplete. It lacks crucial details such as response format beyond 'BibTeX-formatted', error cases, or usage constraints, leaving significant gaps for an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the schema provides no parameter descriptions. The description adds some semantics by explaining that 'query' matches publications and 'results' retrieves citations, but it doesn't specify query syntax, result limits, or default behavior beyond the schema's default value. It partially compensates but not fully.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Retrieve') and resource ('BibTeX-formatted citations for publications'), specifying the format and source (Google Scholar). It doesn't explicitly differentiate from the sibling tool 'get_citeas_data', but the purpose is well-defined and not tautological.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus the sibling 'get_citeas_data', nor any context about prerequisites, alternatives, or exclusions. The description only states what it does, not when it should be used.

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.

  1. 2 tool updatesv1.0.0
    • First observedget_citeas_data
    • First observedget_scholar_data
Deploy Server
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TDQS

B3.1/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have clearly distinct purposes: get_citeas_data retrieves a citation for a specific resource (DOI, URL, keyword), while get_scholar_data searches for multiple publications matching a query. There is no overlap in functionality, making tool selection unambiguous.

Naming Consistency5/5

Both tools follow a consistent verb_noun pattern (get_citeas_data and get_scholar_data), using the same verb 'get' and a descriptive noun indicating the data source. The naming is predictable and uniform throughout the set.

Tool Count2/5

With only two tools, the server feels thin for a citation management domain. While the tools cover basic retrieval from two sources, there are no operations for managing citations (e.g., save, update, delete) or handling metadata, which limits functionality for typical agent workflows.

Completeness2/5

The tool set is severely incomplete for citation management. It only provides retrieval from two sources, lacking essential operations like creating, updating, deleting, or organizing citations. There are no tools for formatting, exporting, or integrating with reference managers, leaving significant gaps in the domain coverage.

Maintenance

ActivityInactive
ResponsivenessNo issues

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