Skip to main content
Glama
enesjakozh
botanicastudios

Crossref MCP Server

by botanicastudios
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Crossref MCP サーバー

Crossref API と対話するためのモデル コンテキスト プロトコル (MCP) サーバー。

特徴

  • タイトルで作品を検索

  • 著者による作品検索

  • DOIで作品の詳細を取得

Related MCP server: ArXiv-MCP

インストール

{
  "mcpServers": {
    "crossref": {
      "command": "npx",
      "args": [
        "-y",
        "@botanicastudios/crossref-mcp"
      ]
    }
  }
}

使用法

サーバーは 3 つの主なツールを提供します。

1. タイトルで検索

Crossref でタイトル別に作品を検索:

// Example: Search for works containing "quantum computing" in the title
{
  "title": "quantum computing",
  "rows": 5  // Optional, defaults to 5
}

2. 著者で検索

Crossrefで著者別に作品を検索:

// Example: Search for works by "Einstein"
{
  "author": "Einstein",
  "rows": 5  // Optional, defaults to 5
}

3. DOIで仕事を得る

DOI を使用して特定の作品を取得します。

// Example: Get work with DOI "10.1088/1742-6596/1398/1/012023"
{
  "doi": "10.1088/1742-6596/1398/1/012023"
}

応答フォーマット

すべての応答は、次の形式の構造化された JSON オブジェクトとして返されます。

検索が成功した場合:

{
  "status": "success",
  "query": {
    /* the original query parameters */
  },
  "count": 5,
  "results": [
    {
      "title": "Work title",
      "authors": [
        {
          "given": "First name",
          "family": "Last name",
          "name": "First name Last name"
        }
      ],
      "published": {
        "dateParts": [2023, 1, 15],
        "dateString": "2023-1-15"
      },
      "type": "journal-article",
      "doi": "10.xxxx/xxxxx",
      "url": "https://doi.org/10.xxxx/xxxxx",
      "container": "Journal Name",
      "publisher": "Publisher Name",
      "issue": "1",
      "volume": "42",
      "abstract": "This is the abstract of the work, if available."
    }
    // additional results...
  ]
}

単一のDOI検索の場合:

{
  "status": "success",
  "query": { "doi": "10.xxxx/xxxxx" },
  "result": {
    // work details as shown above
  }
}

エラーまたは結果がない場合:

{
  "status": "error" | "no_results" | "not_found",
  "message": "Error message" | null,
  "query": { /* the original query parameters */ }
}

テスト

サーバーには、Vitestを使用した包括的なテストスイートが付属しています。テストは利用可能なすべてのツールを網羅し、成功レスポンス、空の結果、エラー処理など、さまざまなシナリオが含まれています。

テストの実行

npm test

テスト構造

このテストでは、Vitestのモック機能を使用して、実際のネットワークリクエストを発行することなく、Crossref APIのレスポンスをシミュレートします。テストの構成は以下のとおりです。

  1. 模擬データ: タイトル検索、著者検索、DOI検索のサンプル応答

  2. モックハンドラー: mcp-server-test-handlers.js内のハンドラー関数のテストバージョン

  3. テストケース: すべてのツールを網羅するテスト:

    • 成功したAPIレスポンス

    • 空の結果セット

    • エラー処理とネットワーク障害

テストの拡張

テストケースを追加するには:

  1. 必要に応じてテストファイルに新しいモックデータを追加します

  2. 関連する記述ブロックに追加のテストケースを作成する

  3. mockFetchResponse()ヘルパーを使用して API レスポンスをシミュレートします

例：

it("should handle a new edge case", async () => {
  // Mock the response
  mockFetchResponse({
    // Your sample response data
  });

  // Call the handler
  const result = await handlers.searchByTitle({ title: "example" });

  // Assert the expected results
  expect(result).toMatchObject({
    // Expected response structure
  });
});

Available Tools

3 tools
getWorkByDOIC

Retrieve a specific scientific paper by its DOI

ParametersJSON Schema
NameRequiredDescriptionDefault
doiYesThe DOI to look up

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 full burden for behavioral disclosure. It states the retrieval action but doesn't mention error handling (e.g., invalid DOI), rate limits, authentication needs, or what happens if the paper isn't found. 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.

Conciseness5/5

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

The description is a single, efficient sentence that gets straight to the point with zero wasted words. It's appropriately sized for this simple lookup tool and front-loads the essential information.

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 insufficiently complete. It doesn't explain what format the paper is returned in (metadata, full text, citation), error conditions, or any limitations. For a retrieval tool with no structured output documentation, more context is needed.

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 100%, so the schema already documents the 'doi' parameter adequately. The description adds no additional parameter semantics beyond what's in the schema, maintaining the baseline score for high schema coverage.

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 action ('Retrieve') and resource ('specific scientific paper by its DOI'), making the purpose immediately understandable. It doesn't explicitly distinguish from sibling tools (searchByAuthor, searchByTitle), but the DOI-based retrieval is inherently different from author/title searches.

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 about when to use this tool versus the sibling search tools. The description implies it's for DOI-based lookups, but there's no explicit comparison or mention of alternatives, leaving the agent to infer usage context.

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

searchByAuthorC

Search for scientific papers by author in Crossref

ParametersJSON Schema
NameRequiredDescriptionDefault
authorYesThe author name to search for
rowsNoNumber of results to return

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 full burden for behavioral disclosure. It states the tool searches but doesn't mention whether it's read-only, what happens on errors, rate limits, authentication needs, or response format. For a search 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.

Conciseness5/5

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 and front-loaded with the core purpose, making it easy for an agent to parse 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 no annotations, no output schema, and a search tool that likely returns complex results, the description is incomplete. It doesn't explain what the search returns, how results are structured, or any behavioral constraints. For a tool with this complexity and lack of structured data, more context is needed.

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 100%, so the schema already documents both parameters ('author' and 'rows') with descriptions. The description doesn't add any parameter details beyond what's in the schema, such as search syntax or result ordering. 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.

Purpose4/5

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

The description clearly states the action ('Search for scientific papers') and resource ('by author in Crossref'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'searchByTitle' or 'getWorkByDOI' beyond the 'by author' qualifier, which is why it doesn't reach 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.

Usage Guidelines2/5

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 'searchByTitle' or 'getWorkByDOI'. There's no mention of use cases, prerequisites, or exclusions, 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.

searchByTitleC

Search for scientific papers by title in Crossref

ParametersJSON Schema
NameRequiredDescriptionDefault
titleYesThe title to search for
rowsNoNumber of results to return

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 full burden for behavioral disclosure. It states the search function but doesn't mention rate limits, authentication requirements, error handling, or what the response format looks like (beyond 'scientific papers'). This leaves significant gaps for a tool with potential external API constraints.

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 that communicates the core purpose without unnecessary words. It's appropriately sized for a simple search tool and front-loads the essential information.

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 insufficiently complete. It doesn't explain what the search returns (beyond 'scientific papers'), how results are structured, or any limitations of the Crossref API. For a search tool with external dependencies, more context is needed.

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 100%, so the input schema already fully documents both parameters. The description adds no additional parameter semantics beyond what's in the schema (e.g., search behavior, title matching rules, result ordering). The baseline score of 3 reflects adequate but minimal value addition.

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 action ('Search for scientific papers') and resource ('by title in Crossref'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'searchByAuthor' or 'getWorkByDOI', which would be needed for 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.

Usage Guidelines2/5

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 'searchByAuthor' or 'getWorkByDOI'. There's no mention of use cases, prerequisites, or comparative advantages, leaving the agent without context for tool selection.

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

Install Server
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

B3.1/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: retrieving a paper by DOI, searching by author, and searching by title. There is no overlap in functionality, making it easy for an agent to select the appropriate tool based on the query.

Naming Consistency4/5

The naming follows a consistent verb_noun pattern (getWorkByDOI, searchByAuthor, searchByTitle), with all tools using camelCase. However, the slight inconsistency in 'getWorkByDOI' (using 'Work' instead of 'Paper' or a more specific term) is a minor deviation from perfect alignment with the domain.

Tool Count3/5

With only 3 tools, the server feels thin for a Crossref domain that typically involves broader operations like searching by keyword, year, or journal. While the tools cover basic retrieval and search, the count is borderline low for comprehensive scientific paper access.

Completeness2/5

The tool surface has significant gaps for a Crossref server. Missing operations include searching by keyword, year, or journal, as well as CRUD-like actions such as listing or filtering results. This incompleteness will likely cause agent failures when handling common academic queries.

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Connectors

Related MCP Servers

  • MCP-DBLP

    szeider
    A
    license
    A
    quality
    B
    maintenance
    A Model Context Protocol server that provides access to the DBLP computer science bibliography database, allowing AI models to search publications, process citations, and generate accurate BibTeX entries.
    6
    43
    MIT

  • ArXiv-MCP

    kelvingao
    A
    license
    Not graded
    quality
    D
    maintenance
    A Model Context Protocol server that enables AI agents to search, retrieve, and analyze academic papers from arXiv, supporting features like keyword search, paper details retrieval, content extraction, and paper analysis.
    4
    MIT

  • Semantic Scholar MCP Server

    alperenkocyigit
    F
    license
    Not graded
    quality
    D
    maintenance
    A comprehensive Model Context Protocol server that provides AI assistants with direct access to Semantic Scholar's academic database, enabling advanced paper discovery, citation analysis, author research, and AI-powered recommendations.
    16
    -

Appeared in Searches

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/botanicastudios/crossref-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server