Suppr (超能文献) は、WildData が提供するAI搭載の学術ツールプラットフォームです。このMCPサーバーは、AIアシスタントにドキュメント翻訳および文献検索機能を提供します。
🌐 AIドキュメント翻訳 — PDF、Word (.docx)、Excel (.xlsx)、PowerPoint (.pptx)、TXT、HTMLドキュメントを13言語間で翻訳します。元のフォーマットを保持し、ソース言語を自動的に検出します。
🔬 PubMed学術検索 — 数百万件の生物医学研究論文を対象としたセマンティック文献検索。DOI、PMID、ジャーナルのインパクトファクター、引用数、著者の所属、要約、論文への直接リンクなどの構造化されたメタデータを返します。
🤖 MCP互換 — Claude Desktop、Cursor、Windsurf、およびすべての Model Context Protocol クライアントで動作します。
npx suppr-mcpRelated MCP server: Paperlib MCP
グローバルインストール：
npm install -g suppr-mcpまたは npx を使用（インストール不要）：
npx suppr-mcpSuppr API にアクセスして、APIキーを取得してください。
export SUPPR_API_KEY=your_api_key_here~/Library/Application Support/Claude/claude_desktop_config.json（macOS）または対応する設定ファイルを編集します：
{
"mcpServers": {
"suppr": {
"command": "npx",
"args": ["-y", "suppr-mcp"],
"env": {
"SUPPR_API_KEY": "your_api_key_here"
}
}
}
}またはグローバルインストールを使用：
{
"mcpServers": {
"suppr": {
"command": "suppr-mcp",
"env": {
"SUPPR_API_KEY": "your_api_key_here"
}
}
}
}ドキュメント翻訳タスクを作成します。
パラメータ：
file_path (file_path と file_url のいずれか必須): ソースファイルのパス
file_url (file_path と file_url のいずれか必須): 翻訳するドキュメントのURL
to_lang (必須): ターゲット言語コード
from_lang (任意): ソース言語コード（デフォルトは自動検出）
optimize_math_formula (任意): 数式を最適化（PDFのみ）
例：
{
"file_url": "https://example.com/document.pdf",
"to_lang": "en",
"from_lang": "zh",
"optimize_math_formula": true
}戻り値：
{
"task_id": "02a6c6d1-3f70-4a5a-80bc-971d53a37bb1",
"status": "INIT",
"consumed_point": 453,
"source_lang": "zh",
"target_lang": "en",
"optimize_math_formula": true
}翻訳タスクの詳細情報とステータスを取得します。
パラメータ：
task_id (必須): 翻訳タスクID
例：
{
"task_id": "02a6c6d1-3f70-4a5a-80bc-971d53a37bb1"
}戻り値：
{
"task_id": "02a6c6d1-3f70-4a5a-80bc-971d53a37bb1",
"status": "DONE",
"progress": 1.0,
"consumed_point": 453,
"source_file_name": "document.pdf",
"source_file_url": "https://example.com/source.pdf",
"target_file_url": "https://example.com/translated.pdf",
"source_lang": "zh",
"target_lang": "en",
"error_msg": null,
"optimize_math_formula": true
}タスクステータスの説明：
INIT: 初期化中
PROGRESS: 進行中
DONE: 完了
ERROR: エラー
翻訳タスクのリストを取得します（ページネーション対応）。
パラメータ：
offset (任意): ページネーションのオフセット、デフォルトは0
limit (任意): 1ページあたりの件数、デフォルトは20
例：
{
"offset": 0,
"limit": 10
}戻り値：
{
"total": 42,
"offset": 0,
"limit": 10,
"list": [
{
"task_id": "...",
"status": "DONE",
"progress": 1.0,
...
}
]
}AI駆動型の文献セマンティック検索。
パラメータ：
query (必須): 自然言語によるクエリ
topk (任意): 最大結果数（1-100、デフォルトは20）
return_doc_keys (任意): 返却するフィールドを指定
auto_select (任意): 最適な結果を自動選択（デフォルトはtrue）
例：
{
"query": "糖尿病最新研究进展",
"topk": 5,
"return_doc_keys": ["title", "abstract", "doi", "authors"],
"auto_select": true
}利用可能な戻り値フィールド：
title: タイトル
abstract: 要約
authors: 著者リスト
doi: DOI
pmid: PubMed ID
link: リンク
publication: 出版物
pub_year: 出版年
その他のフィールドはAPIドキュメントを参照してください
戻り値：
{
"search_items": [
{
"doc": {
"title": "...",
"abstract": "...",
"authors": [...],
"doi": "...",
...
},
"search_gateway": "pubmed"
}
],
"consumed_points": 20
}一般的な言語コード：
en: English (英語)
zh: Chinese (中国語)
ko: Korean (韓国語)
ja: Japanese (日本語)
fr: French (フランス語)
de: German (ドイツ語)
es: Spanish (スペイン語)
ru: Russian (ロシア語)
ar: Arabic (アラビア語)
pt: Portuguese (ポルトガル語)
it: Italian (イタリア語)
auto: 自動検出
すべてのエラーは標準形式で返されます：
{
"code": 非零错误码,
"msg": "错误信息",
"data": null
}一般的なエラー：
401: APIキーが無効、または提供されていません
400: リクエストパラメータエラー
404: リソースが存在しません
APIキーを設定後、Claude Desktopを再起動します
チャットでツールを使用：
ドキュメントの翻訳：
このドキュメントを翻訳してください：https://example.com/paper.pdf、英語に翻訳して
文献の検索：
"医学画像におけるディープラーニングの応用"に関する最新の文献を検索して
翻訳ステータスの確認：
タスク 02a6c6d1-3f70-4a5a-80bc-971d53a37bb1 の翻訳進捗を確認して
A: https://suppr.wilddata.cn/api-keys にアクセスして登録し、APIキーを取得してください。
A: PDF, DOCX, PPTX, XLSX, HTML, TXT, EPUBなどの一般的な形式をサポートしています。
A: ドキュメントのサイズによりますが、通常数分から十数分程度です。get_translation を使用して進捗を確認できます。
A: 翻訳完了後、get_translation が target_file_url を返します。そのリンクに直接アクセスしてダウンロードしてください。
A: Node.jsのバージョンが18.0.0以上であることを確認し、SUPPR_API_KEY環境変数が設定されていることを確認してください。
Zoteroプラグイン : https://github.com/WildDataX/suppr-zotero-plugin
AIドキュメント翻訳: https://suppr.wilddata.cn/translate/upload
PubMed検索: https://suppr.wilddata.cn/
GitHub組織：WildDataX
サポートが必要な場合は、こちらまでご連絡ください：IT@wilddata.cn
Made with ❤️ by WildData
プロダクト | リンク |
🌐 Supprプラットフォーム | |
📖 APIドキュメント | |
🔌 Zoteroプラグイン | |
🤖 Claude Codeスキル | |
🔬 ディープリサーチ | |
📄 AI翻訳 | |
🔎 PubMed検索 |
Create a document translation task. Supports file upload via path or URL.
| Name | Required | Description | Default |
|---|---|---|---|
| to_lang | Yes | Target language code (required), e.g., en, zh, ko, ja | |
| file_url | No | Document URL to translate (mutually exclusive with file_path) | |
| file_path | No | Local file path to translate (mutually exclusive with file_url) | |
| from_lang | No | Source language code (optional, auto-detect if not specified) | |
| optimize_math_formula | No | Optimize math formulas (PDF only) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden of behavioral disclosure. It only mentions file upload capability and does not disclose side effects (e.g., task creation, asynchronous processing), required permissions, or how to track the resulting task.
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 two short sentences, front-loaded with the purpose and immediately following with input constraints. No unnecessary words 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 create operation with no output schema and no annotations, the description lacks details about the response (e.g., task ID), follow-up steps (e.g., use get_translation to check status), or any workflow context. This leaves the agent uncertain about what happens after invocation.
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 baseline is 3. The description adds minimal meaning beyond the schema; 'file upload via path or URL' summarizes the file_path/file_url mutual exclusion, but this is already documented in the schema. No extra parameter context is provided.
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?
Clearly states the verb 'create' and resource 'document translation task', making it distinct from sibling tools (get_translation, list_translations, search_documents). The added 'Supports file upload via path or URL' further specifies the tool's scope.
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?
No guidance on when to use this tool versus siblings. There is no mention of get/list for retrieving tasks or search_documents for finding documents. The usage is only implied by the tool's name/verb, 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 translation task details and status. Use this to check progress and get result URLs.
| Name | Required | Description | Default |
|---|---|---|---|
| task_id | Yes | Translation task ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full disclosure burden. It implies a read-only operation via the verb 'Get' and mentions the return content (details, status, result URLs), but it does not explicitly state the absence of side effects or address error cases. More explicit transparency would be beneficial.
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 two concise sentences, front-loaded with the core purpose followed by a usage hint. Every word earns its place with no redundant content.
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 simple 1-parameter read tool with no output schema, the description covers the essential aspects: what it does, when to use it, and what it returns (details, status, result URLs). It does not enumerate possible statuses, but this is not critical for invoking the tool correctly.
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 schema already provides a description for task_id ('Translation task ID') with 100% coverage. The tool description adds no further meaning about how to obtain or format the task_id, so it does not exceed the baseline.
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 a specific verb ('Get') and resource ('translation task details and status'), clearly distinguishing this from sibling tools like create_translation, list_translations, and search_documents. It focuses on a single task's details, making its 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 explicit usage context: 'Use this to check progress and get result URLs.' This tells the agent when to invoke the tool, though it doesn't contrast with alternatives or state exclusions. Given the sibling tools, this is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List translation tasks with pagination. View all historical translation tasks.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Results per page (default: 20) | |
| offset | No | Pagination offset (default: 0) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden. It transparently indicates a read-only list operation with pagination, but adds no details on ordering, status scope, or return format. The term 'historical' is ambiguous and could mislead about whether in-progress tasks are included.
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?
Two short sentences with no fluff. The first sentence states the action and key feature (pagination); the second adds the 'historical' scope. Efficient and front-loaded.
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 simple 2-parameter list tool with no output schema or annotations, the description gives enough to understand the operation. However, it does not explain what data is returned, what 'historical' means, or how it relates to sibling search_documents, leaving some gaps.
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 input schema already provides 100% coverage with clear descriptions for 'limit' and 'offset'. The description's mention of pagination adds marginal value beyond the schema, so the baseline score of 3 is appropriate.
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 lists translation tasks and supports pagination. 'List' sharply contrasts with sibling 'get_translation' and 'create_translation', making it easy for an agent to select it for multi-result retrieval.
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?
It conveys the core use case: listing all historical tasks with pagination. While it does not explicitly name alternatives or exclusions, the context 'view all historical translation tasks' gives clear guidance for when to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
AI-driven semantic search for academic literature. Input natural language query to find relevant papers.
| Name | Required | Description | Default |
|---|---|---|---|
| topk | No | Max results to return (1-100, default: 20) | |
| query | Yes | Natural language query, e.g., "latest diabetes research" | |
| auto_select | No | Auto-select best results (default: true) | |
| return_doc_keys | No | Specific fields to return, e.g., ["title", "abstract", "doi"] |
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 reveals that the search is 'AI-driven' and 'semantic,' which is useful, but it does not mention whether the operation is read-only, requires authentication, or what the response contains (e.g., list of papers, metadata). For a search tool, this is a notable gap.
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 one concise sentence that front-loads the core purpose. Every word contributes meaning without redundancy. It is appropriately sized for a straightforward search tool.
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?
The tool is relatively simple with four parameters all documented in the schema, and the description covers the primary purpose. However, without annotations or an output schema, the description omits behavioral details like return format, pagination, or limitations. It is minimally complete but leaves open questions.
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 baseline is 3. The description itself does not add any parameter-specific meaning beyond the schema; it only says to input a natural language query. The schema already documents each parameter thoroughly, so the description's lack of parameter detail is acceptable.
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 performs AI-driven semantic search for academic literature, using natural language queries to find relevant papers. It specifies the verb (search), resource (academic literature), and how to invoke it. However, it does not explicitly differentiate from sibling tools, though the siblings are translation-focused, so differentiation is apparent from context.
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: input a natural language query to find papers. It gives a basic how-to but does not outline when to use this tool versus alternatives or state any exclusions. The sibling tools are translation-related, implying search is for finding papers, but no explicit guidance 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.
v1.1.7create_translationget_translationlist_translationssearch_documentsScored across 4 tools
Each tool has a clear, distinct purpose: create, get, and list translations, plus search documents. There is no overlap or ambiguity between them.
All tool names follow a consistent verb_noun pattern: create_translation, get_translation, list_translations, and search_documents. No mixed conventions.
Four tools is a reasonable size for a server handling translation tasks and document search. It feels slightly minimal but each tool serves a distinct purpose.
The translation lifecycle covers create, get, and list, but lacks update/delete/cancel operations. The search_documents tool seems unrelated to translations, creating a mixed domain with notable gaps.
Translate PDFs, scans, Office files and EPUB across 475 languages and styles, preserving layout.
Search 36M+ PubMed biomedical articles and ClinicalTrials.gov studies.
Search PubMed/Europe PMC, fetch articles and full text (PMC/EPMC/Unpaywall), citations, MeSH terms.
Turn documents into structured, AI-ready data by parsing, enriching, chunking, and embedding.
