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MCP-DBLP

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Python
Hybrid

MCP-DBLP

MCP Compatible License: MIT Python Version

大規模言語モデル（LLM）向けにDBLPコンピュータサイエンス文献データベースへのアクセスを提供するModel Context Protocol (MCP) サーバーです（AI4SC @ AAAI-26 に採択された論文に関連しています）。

概要

MCP-DBLPは、Model Context Protocolを通じてDBLP (Digital Bibliography & Library Project) APIとLLMを統合し、AIモデルが以下のことを行えるようにします：

  • DBLPデータベースからの学術論文の検索と取得

  • 引用の処理とBibTeXエントリの生成

  • 論文タイトルと著者名に対するあいまい一致検索

  • 書誌情報の抽出とフォーマット

  • 文書内の埋め込み参照の処理

  • LLMによる処理を介さない直接的なBibTeXエクスポートによる最大限の正確性の確保

Related MCP server: ArXiv-MCP

機能

  • ブール演算子を使用した包括的な検索機能

  • タイトルと著者名のあいまい一致検索

  • DBLPから直接取得するBibTeXエントリ

  • 年および会議・ジャーナル（venue）による論文フィルタリング

  • 論文データの統計分析

  • LLMによる処理を介さない直接的なBibTeXエクスポート機能による最大限の正確性の確保

利用可能なツール

ツール名

説明

get_instructions

使用方法とワークフローのガイダンスを取得する

search

ブールクエリを使用してDBLPで論文を検索する

fuzzy_title_search

タイトルのあいまい一致で論文を検索する

get_author_publications

特定の著者の論文を取得する

get_venue_info

論文の掲載先（会議・ジャーナル）の詳細情報を取得する

add_bibtex_entry

DBLPキーを使用してコレクションにBibTeXエントリを追加する

export_bibtex

収集したすべてのBibTeXエントリを.bibファイルにエクスポートする

フィードバック

こちらのフォームから作成者へフィードバックをお送りください。

システム要件

  • Python 3.11+

  • uv

インストール

Claude Code

単に以下を実行してください：

claude mcp add mcp-dblp -- uvx mcp-dblp

Claude Desktop

Claude Desktopの設定ファイルに追加してください：

  • macOS/Linux: ~/Library/Application Support/Claude/claude_desktop_config.json

  • Windows: %APPDATA%\Claude\claude_desktop_config.json

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

ソースから（開発用）

git clone https://github.com/szeider/mcp-dblp.git
cd mcp-dblp
uv venv && source .venv/bin/activate
uv pip install -e .

その後、Claude Desktopを以下のように設定します：

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

手順

使用手順は get_instructions ツールから入手できます。主要なワークフローのポイントはツール説明に記載されています。詳細についてはツールを呼び出してください。また、instructions_prompt.mdも参照してください。

ツールの詳細

search

ブールクエリ文字列を使用してDBLPで論文を検索します。

パラメータ:

  • query (string, 必須): 'and' や 'or' といったブール演算子（大文字小文字を区別しない）を含むクエリ文字列

  • max_results (number, オプション): 返す論文の最大数。デフォルトは10

  • year_from (number, オプション): 出版年の下限

  • year_to (number, オプション): 出版年の上限

  • venue_filter (string, オプション): 掲載先（venue）の大文字小文字を区別しない部分一致フィルタ（例: 'iclr'）

  • include_bibtex (boolean, オプション): 結果にBibTeXエントリを含めるかどうか。デフォルトはfalse

fuzzy_title_search

タイトルのあいまい一致を使用してDBLPで論文を検索します。

パラメータ:

  • title (string, 必須): 論文の完全または部分的なタイトル（大文字小文字を区別しない）

  • similarity_threshold (number, 必須): 0から1の間の浮動小数点数。1.0は完全一致を意味する

  • max_results (number, オプション): 返す論文の最大数。デフォルトは10

  • year_from (number, オプション): 出版年の下限

  • year_to (number, オプション): 出版年の上限

  • venue_filter (string, オプション): 掲載先の大文字小文字を区別しない部分一致フィルタ

  • include_bibtex (boolean, オプション): 結果にBibTeXエントリを含めるかどうか。デフォルトはfalse

get_author_publications

あいまい一致を使用して、特定の著者の論文詳細を取得します。

パラメータ:

  • author_name (string, 必須): 著者名の完全または部分的な名前（大文字小文字を区別しない）

  • similarity_threshold (number, 必須): 0から1の間の浮動小数点数。1.0は完全一致を意味する

  • max_results (number, オプション): 返す論文の最大数。デフォルトは20

  • include_bibtex (boolean, オプション): 結果にBibTeXエントリを含めるかどうか。デフォルトはfalse

get_venue_info

論文の掲載先（venue）に関する詳細情報を取得します。

パラメータ:

  • venue_name (string, 必須): 掲載先名または略称（例: 'ICLR' または正式名称）

add_bibtex_entry

後でエクスポートするために、BibTeXエントリをコレクションに追加します。

パラメータ:

  • dblp_key (string, 必須): 検索結果からのDBLPキー（例: "conf/nips/VaswaniSPUJGKP17"）

  • citation_key (string, 必須): .bibファイルで使用する引用キー（例: "Vaswani2017"）

動作:

  • 提供されたキーを使用してDBLPから直接BibTeXエントリを取得します

  • 引用キーをカスタムキーに置き換えます

  • セッションコレクションに追加します（重複するキーは上書きされます）

  • コレクションの数とともに、成功/失敗のフィードバックを即座に返します

  • 失敗した個別のエントリの再試行が可能です

export_bibtex

収集したすべてのBibTeXエントリを.bibファイルにエクスポートします。

パラメータ:

  • path (string, 必須): .bibファイルの絶対パス（例: "/path/to/refs.bib"）

動作:

  • add_bibtex_entry を通じて追加されたすべてのエントリを指定されたパスに保存します

  • .bib拡張子がない場合は自動的に追加されます

  • 必要に応じて親ディレクトリが作成されます

  • エクスポート成功後にコレクションをクリアします

  • 保存されたファイルのフルパスを返します

  • コレクションが空の場合はエラーを返します

重要な注意: BibTeXエントリは10秒のタイムアウト保護付きでDBLPから直接取得され、LLMによって処理、変更、またはハルシネーション（幻覚）されることはありません。これにより、書誌データの最大限の正確性と信頼性が保証されます。引用キーのみが指定通りに変更されます。リクエストがタイムアウトした場合、エラーメッセージが返され、そのエントリはコレクションに追加されません。

例

入力テキスト:

Our exploration focuses on two types of explanation problems, abductive and contrastive, in local and global contexts (Marques-Silva 2023). Abductive explanations (Ignatiev, Narodytska, and Marques-Silva 2019), corresponding to prime-implicant explanations (Shih, Choi, and Darwiche 2018) and sufficient reason explanations (Darwiche and Ji 2022), clarify specific decision-making instances, while contrastive explanations (Miller 2019; Ignatiev et al. 2020), corresponding to necessary reason explanations (Darwiche and Ji 2022), make explicit the reasons behind the non-selection of alternatives. Conversely, global explanations (Ribeiro, Singh, and Guestrin 2016; Ignatiev, Narodytska, and Marques-Silva 2019) aim to unravel models' decision patterns across various inputs.

出力テキスト:

Our exploration focuses on two types of explanation problems, abductive and contrastive, in local and global contexts \cite{MarquesSilvaI23}. Abductive explanations \cite{IgnatievNM19}, corresponding to prime-implicant explanations \cite{ShihCD18} and sufficient reason explanations \cite{DarwicheJ22}, clarify specific decision-making instances, while contrastive explanations \cite{Miller19}; \cite{IgnatievNA020}, corresponding to necessary reason explanations \cite{DarwicheJ22}, make explicit the reasons behind the non-selection of alternatives. Conversely, global explanations \cite{Ribeiro0G16}; \cite{IgnatievNM19} aim to unravel models' decision patterns across various inputs.

出力 Bibtex

All references have been successfully exported to a BibTeX file at: /absolute/path/to/bibtex/20250305_231431.bib

@article{MarquesSilvaI23,
 author       = {Jo{\~{a}}o Marques{-}Silva and
                 Alexey Ignatiev},
 title        = {No silver bullet: interpretable {ML} models must be explained},
 journal      = {Frontiers Artif. Intell.},
 volume       = {6},
 year         = {2023},
 url          = {https://doi.org/10.3389/frai.2023.1128212},
 doi          = {10.3389/FRAI.2023.1128212},
 timestamp    = {Tue, 07 May 2024 20:23:47 +0200},
 biburl       = {https://dblp.org/rec/journals/frai/MarquesSilvaI23.bib},
 bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{IgnatievNM19,
 author       = {Alexey Ignatiev and
                 Nina Narodytska and
                 Jo{\~{a}}o Marques{-}Silva},
 title        = {Abduction-Based Explanations for Machine Learning Models},
 booktitle    = {The Thirty-Third {AAAI} Conference on Artificial Intelligence, {AAAI}
                 2019, The Thirty-First Innovative Applications of Artificial Intelligence
                 Conference, {IAAI} 2019, The Ninth {AAAI} Symposium on Educational
                 Advances in Artificial Intelligence, {EAAI} 2019, Honolulu, Hawaii,
                 USA, January 27 - February 1, 2019},
 pages        = {1511--1519},
 publisher    = {{AAAI} Press},
 year         = {2019},
 url          = {https://doi.org/10.1609/aaai.v33i01.33011511},
 doi          = {10.1609/AAAI.V33I01.33011511},
 timestamp    = {Mon, 04 Sep 2023 12:29:24 +0200},
 biburl       = {https://dblp.org/rec/conf/aaai/IgnatievNM19.bib},
 bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{ShihCD18,
 author       = {Andy Shih and
                 Arthur Choi and
                 Adnan Darwiche},
 editor       = {J{\'{e}}r{\^{o}}me Lang},
 title        = {A Symbolic Approach to Explaining Bayesian Network Classifiers},
 booktitle    = {Proceedings of the Twenty-Seventh International Joint Conference on
                 Artificial Intelligence, {IJCAI} 2018, July 13-19, 2018, Stockholm,
                 Sweden},
 pages        = {5103--5111},
 publisher    = {ijcai.org},
 year         = {2018},
 url          = {https://doi.org/10.24963/ijcai.2018/708},
 doi          = {10.24963/IJCAI.2018/708},
 timestamp    = {Tue, 20 Aug 2019 16:19:08 +0200},
 biburl       = {https://dblp.org/rec/conf/ijcai/ShihCD18.bib},
 bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DarwicheJ22,
 author       = {Adnan Darwiche and
                 Chunxi Ji},
 title        = {On the Computation of Necessary and Sufficient Explanations},
 booktitle    = {Thirty-Sixth {AAAI} Conference on Artificial Intelligence, {AAAI}
                 2022, Thirty-Fourth Conference on Innovative Applications of Artificial
                 Intelligence, {IAAI} 2022, The Twelveth Symposium on Educational Advances
                 in Artificial Intelligence, {EAAI} 2022 Virtual Event, February 22
                 - March 1, 2022},
 pages        = {5582--5591},
 publisher    = {{AAAI} Press},
 year         = {2022},
 url          = {https://doi.org/10.1609/aaai.v36i5.20498},
 doi          = {10.1609/AAAI.V36I5.20498},
 timestamp    = {Mon, 04 Sep 2023 16:50:24 +0200},
 biburl       = {https://dblp.org/rec/conf/aaai/DarwicheJ22.bib},
 bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{Miller19,
 author       = {Tim Miller},
 title        = {Explanation in artificial intelligence: Insights from the social sciences},
 journal      = {Artif. Intell.},
 volume       = {267},
 pages        = {1--38},
 year         = {2019},
 url          = {https://doi.org/10.1016/j.artint.2018.07.007},
 doi          = {10.1016/J.ARTINT.2018.07.007},
 timestamp    = {Thu, 25 May 2023 12:52:41 +0200},
 biburl       = {https://dblp.org/rec/journals/ai/Miller19.bib},
 bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{IgnatievNA020,
 author       = {Alexey Ignatiev and
                 Nina Narodytska and
                 Nicholas Asher and
                 Jo{\~{a}}o Marques{-}Silva},
 editor       = {Matteo Baldoni and
                 Stefania Bandini},
 title        = {From Contrastive to Abductive Explanations and Back Again},
 booktitle    = {AIxIA 2020 - Advances in Artificial Intelligence - XIXth International
                 Conference of the Italian Association for Artificial Intelligence,
                 Virtual Event, November 25-27, 2020, Revised Selected Papers},
 series       = {Lecture Notes in Computer Science},
 volume       = {12414},
 pages        = {335--355},
 publisher    = {Springer},
 year         = {2020},
 url          = {https://doi.org/10.1007/978-3-030-77091-4\_21},
 doi          = {10.1007/978-3-030-77091-4\_21},
 timestamp    = {Tue, 15 Jun 2021 17:23:54 +0200},
 biburl       = {https://dblp.org/rec/conf/aiia/IgnatievNA020.bib},
 bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{Ribeiro0G16,
 author       = {Marco T{\'{u}}lio Ribeiro and
                 Sameer Singh and
                 Carlos Guestrin},
 editor       = {Balaji Krishnapuram and
                 Mohak Shah and
                 Alexander J. Smola and
                 Charu C. Aggarwal and
                 Dou Shen and
                 Rajeev Rastogi},
 title        = {"Why Should {I} Trust You?": Explaining the Predictions of Any Classifier},
 booktitle    = {Proceedings of the 22nd {ACM} {SIGKDD} International Conference on
                 Knowledge Discovery and Data Mining, San Francisco, CA, USA, August
                 13-17, 2016},
 pages        = {1135--1144},
 publisher    = {{ACM}},
 year         = {2016},
 url          = {https://doi.org/10.1145/2939672.2939778},
 doi          = {10.1145/2939672.2939778},
 timestamp    = {Fri, 25 Dec 2020 01:14:16 +0100},
 biburl       = {https://dblp.org/rec/conf/kdd/Ribeiro0G16.bib},
 bibsource    = {dblp computer science bibliography, https://dblp.org}
}

免責事項

このMCP-DBLPはプロトタイプ段階であり、注意して使用する必要があります。ユーザーは実験することを推奨しますが、重要な環境での使用は自己責任で行ってください。

ライセンス

このプロジェクトはMITライセンスの下でライセンスされています。詳細は LICENSE ファイルを参照してください。

Available Tools

6 tools
calculate_statisticsA

Calculate statistics from a list of publication results. Arguments:

  • results (array, required): An array of publication objects, each with at least 'title', 'authors', 'venue', and 'year'. Returns a dictionary with:

  • total_publications: Total count.

  • time_range: Dictionary with 'min' and 'max' publication years.

  • top_authors: List of tuples (author, count) sorted by count.

  • top_venues: List of tuples (venue, count) sorted by count (empty venue is treated as '(empty)').

ParametersJSON Schema
NameRequiredDescriptionDefault
resultsYes

TDQS

A3.6/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It discloses the return structure (a dictionary with specific keys) and behavioral details like how empty venues are treated. However, it doesn't mention error handling, performance aspects (e.g., for large arrays), or side effects. The description adds some context but isn't comprehensive.

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 appropriately sized and front-loaded with the purpose, followed by structured details on arguments and returns. Every sentence earns its place by clarifying inputs and outputs, though it could be slightly more concise by integrating the argument list into the flow rather than as a separate bullet.

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

Completeness4/5

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

Given 1 parameter with 0% schema coverage and no output schema, the description does well by fully explaining the parameter and return values. It covers the tool's complexity adequately, though it could improve by adding usage context or error scenarios. The lack of annotations and output schema is compensated by the detailed description.

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

Parameters5/5

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 fully. It provides detailed semantics for the single parameter 'results', specifying it as an array of publication objects with required fields ('title', 'authors', 'venue', 'year'). This adds significant meaning beyond the bare schema, fully documenting the parameter's structure and expectations.

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 tool's purpose: 'Calculate statistics from a list of publication results.' It specifies the verb ('calculate') and resource ('statistics'), but doesn't explicitly differentiate from siblings like 'search' or 'get_author_publications' which have different functions. The purpose is clear but lacks sibling comparison.

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 alternatives. The description doesn't mention prerequisites (e.g., needing publication data first), exclusions, or compare to siblings like 'export_bibtex' or 'get_venue_info'. Usage is implied from the purpose 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.

export_bibtexA

Export BibTeX entries from a collection of HTML hyperlinks. Arguments:

  • links (string, required): HTML string containing one or more key links. The href attribute should contain a URL to a BibTeX file, and the link text is used as the citation key. Example input with three links: "Smith2023 Jones2022 Brown2021" Process:

  • For each link, the tool fetches the BibTeX content from the URL

  • The citation key in each BibTeX entry is replaced with the key from the link text

  • All entries are combined and saved to a .bib file with a timestamp filename Returns:

  • A message with the full path to the saved .bib file

ParametersJSON Schema
NameRequiredDescriptionDefault
linksYes

TDQS

A4.2/5.0
Behavior4/5

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 effectively describes the process: fetching BibTeX content from URLs, replacing citation keys, saving to a timestamped .bib file, and returning the file path. It covers key behaviors like network fetching and file creation, though it omits details like error handling or rate limits.

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 well-structured with sections for Arguments, Process, and Returns, making it easy to parse. It is appropriately sized, with each sentence adding value, though it could be slightly more concise by integrating the example more seamlessly.

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

Completeness4/5

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

Given the complexity (network fetching, file creation) and lack of annotations or output schema, the description is largely complete. It explains the process, parameter usage, and return value. However, it could improve by mentioning potential errors (e.g., invalid URLs) or file format specifics.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must fully compensate. It provides detailed semantics for the single parameter 'links', including its type, requirement, format (HTML string with <a> tags), example, and how the href and link text are used. This adds significant meaning beyond the basic schema.

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

Purpose5/5

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

The description clearly states the tool exports BibTeX entries from HTML hyperlinks, specifying the exact verb ('export'), resource ('BibTeX entries'), and source ('collection of HTML hyperlinks'). It distinguishes from sibling tools like 'get_author_publications' or 'search' by focusing on BibTeX extraction from links rather than general searches or author-specific queries.

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

Usage Guidelines3/5

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

The description implies usage when BibTeX entries need to be exported from HTML links, but it does not explicitly state when to use this tool versus alternatives like 'fuzzy_title_search' or 'get_author_publications'. It provides an example input, which helps clarify context, 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.

get_author_publicationsA

Retrieve publication details for a specific author with fuzzy matching. Arguments:

  • author_name (string, required): Full or partial author name (case-insensitive).

  • similarity_threshold (number, required): A float between 0 and 1 where 1.0 means an exact match.

  • max_results (number, optional): Maximum number of publications to return. Default is 20.

  • include_bibtex (boolean, optional): Whether to include BibTeX entries in the results. Default is false. Returns a dictionary with keys: name, publication_count, publications, and stats (which includes top venues, years, and types).

ParametersJSON Schema
NameRequiredDescriptionDefault
author_nameYes
include_bibtexNo
max_resultsNo
similarity_thresholdYes

TDQS

A3.6/5.0
Behavior3/5

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 describes key behaviors like fuzzy matching, case-insensitive search, and default values for optional parameters. However, it lacks details on error handling, rate limits, authentication needs, or what happens with low similarity thresholds. The description doesn't contradict annotations, but it's incomplete for a tool with fuzzy matching and multiple parameters.

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 well-structured and appropriately sized. It starts with a clear purpose statement, then lists arguments with detailed explanations, and ends with return value information. Every sentence adds value, though the return details could be slightly more concise. It's front-loaded with the core functionality.

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

Completeness4/5

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

Given the tool's complexity (fuzzy matching, 4 parameters) and lack of annotations/output schema, the description does a good job of covering key aspects. It explains parameters thoroughly and outlines the return structure. However, it could benefit from more behavioral context (e.g., performance implications, error cases) to be fully complete for an agent's use.

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

Parameters5/5

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

The description adds significant value beyond the input schema, which has 0% description coverage. It explains each parameter's purpose: 'author_name' for full/partial name matching, 'similarity_threshold' as a float between 0-1 for match precision, 'max_results' for limiting output with a default, and 'include_bibtex' for including BibTeX entries. This compensates fully for the schema's lack of descriptions.

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 tool's purpose: 'Retrieve publication details for a specific author with fuzzy matching.' It specifies the verb ('retrieve'), resource ('publication details'), and key behavior ('fuzzy matching'). However, it doesn't explicitly differentiate from sibling tools like 'fuzzy_title_search' or 'search', which might have overlapping functionality.

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. It doesn't mention sibling tools like 'fuzzy_title_search' (for titles) or 'search' (which might be more general), nor does it specify prerequisites or exclusions. Usage is implied by the description 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_venue_infoA

Retrieve detailed information about a publication venue. Arguments:

  • venue_name (string, required): Venue name or abbreviation (e.g., 'ICLR' or full name). Returns a dictionary with fields: abbreviation, name, publisher, type, and category. Note: Some fields may be empty if DBLP does not provide the information.

ParametersJSON Schema
NameRequiredDescriptionDefault
venue_nameYes

TDQS

A3.8/5.0
Behavior3/5

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 that the tool retrieves data from DBLP and notes that some fields may be empty, adding useful behavioral context about data source and completeness. However, it lacks details on error handling, rate limits, or authentication needs, which are important 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.

Conciseness4/5

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

The description is well-structured with clear sections for arguments and returns, and every sentence adds value. It could be slightly more front-loaded by moving the note about DBLP earlier, but overall it's efficient with minimal waste.

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

Completeness4/5

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

Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is reasonably complete. It covers the purpose, parameter semantics, return fields, and data source limitations. However, it could improve by mentioning error cases or when to use alternatives, slightly reducing completeness.

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

Parameters5/5

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

The description adds significant meaning beyond the input schema, which has 0% coverage. It explains the 'venue_name' parameter as accepting names or abbreviations (e.g., 'ICLR'), clarifies it's required, and provides examples, fully compensating for the schema's lack of documentation.

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

Purpose5/5

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 'detailed information about a publication venue,' making the purpose specific and unambiguous. It distinguishes this tool from siblings like 'get_uthor_publications' or 'search' by focusing on venue metadata rather than author data or broader 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 on when to use this tool versus alternatives like 'search' or 'fuzzy_title_search.' The description implies usage for venue details but lacks explicit context, prerequisites, or exclusions, leaving the agent to infer based on tool names alone.

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. 6 tool updatesv1.0.0
    • First observedcalculate_statistics
    • First observedexport_bibtex
    • First observedfuzzy_title_search
    • First observedget_author_publications
    • First observedget_venue_info
    • First observedsearch
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.1/5.0

Scored across 6 tools

Disambiguation5/5

Each tool has a clearly distinct purpose with no ambiguity: calculate_statistics processes existing results, export_bibtex handles BibTeX export, fuzzy_title_search and search provide different search methods, get_author_publications focuses on authors, and get_venue_info targets venues. The tools cover different aspects of the DBLP domain without overlap.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern with snake_case: calculate_statistics, export_bibtex, fuzzy_title_search, get_author_publications, get_venue_info, and search. The naming is predictable and readable throughout the set.

Tool Count5/5

With 6 tools, the count is well-scoped for a DBLP server, covering key operations like search, author/venue info, statistics, and BibTeX export. Each tool earns its place without feeling thin or bloated, suitable for typical academic workflows.

Completeness4/5

The tool set provides strong coverage for core DBLP operations including search, author/venue retrieval, and data export, with minor gaps such as no direct tool for updating or deleting data (though this may be intentional for a read-heavy domain). Agents can effectively navigate publication workflows with these tools.

Maintenance

ActivityMaintained
ResponsivenessUnresponsive

Resources

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