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MCP-DBLP

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Python
Hybrid

MCP-DBLP

MCP Compatible License: MIT Python Version

一个模型上下文协议 (MCP) 服务器，为大语言模型提供对 DBLP 计算机科学文献数据库的访问（相关论文已被 AI4SC @ AAAI-26 接收）。

概述

MCP-DBLP 通过模型上下文协议将 DBLP（数字书目与图书馆项目）API 与 LLM 集成，使 AI 模型能够：

  • 从 DBLP 数据库搜索并检索学术出版物

  • 处理引用并生成 BibTeX 条目

  • 对出版物标题和作者姓名执行模糊匹配

  • 提取并格式化书目信息

  • 处理文档中嵌入的参考文献

  • 直接导出 BibTeX，绕过 LLM 处理以实现最高准确度

Related MCP server: ArXiv-MCP

功能

  • 具有布尔查询功能的全面搜索能力

  • 标题和作者姓名的模糊匹配

  • 直接从 DBLP 检索 BibTeX 条目

  • 按年份和出版场所筛选出版物

  • 出版物数据的统计分析

  • 直接导出 BibTeX 功能，绕过 LLM 处理以实现最高准确度

可用工具

工具名称

描述

get_instructions

获取使用说明和工作流程指南

search

使用布尔查询在 DBLP 中搜索出版物

fuzzy_title_search

使用标题模糊匹配搜索出版物

get_author_publications

检索特定作者的出版物

get_venue_info

获取关于出版场所的详细信息

add_bibtex_entry

通过 DBLP 键将 BibTeX 条目添加到集合中

export_bibtex

将所有收集的 BibTeX 条目导出到 .bib 文件

反馈

通过此 表单 向作者提供反馈。

系统要求

  • Python 3.11+

  • uv

安装

Claude Code

只需运行：

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

Claude Desktop

添加到您的 Claude Desktop 配置文件中：

  • 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"]
    }
  }
}

从源码安装（开发）

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

然后配置 Claude Desktop：

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

说明

使用说明可通过 get_instructions 工具获取。关键工作流程点显示在工具描述中；调用该工具以获取完整详细信息。另请参阅 instructions_prompt.md。

工具详情

search

使用布尔查询字符串在 DBLP 中搜索出版物。

参数：

  • query (string, 必需): 包含布尔运算符 'and' 和 'or'（不区分大小写）的查询字符串

  • max_results (number, 可选): 返回的最大出版物数量。默认为 10

  • year_from (number, 可选): 出版年份下限

  • year_to (number, 可选): 出版年份上限

  • venue_filter (string, 可选): 出版场所的不区分大小写子字符串过滤器（例如 'iclr'）

  • include_bibtex (boolean, 可选): 是否在结果中包含 BibTeX 条目。默认为 false

fuzzy_title_search

使用标题模糊匹配在 DBLP 中搜索出版物。

参数：

  • title (string, 必需): 出版物的完整或部分标题（不区分大小写）

  • similarity_threshold (number, 必需): 0 到 1 之间的浮点数，1.0 表示完全匹配

  • max_results (number, 可选): 返回的最大出版物数量。默认为 10

  • year_from (number, 可选): 出版年份下限

  • year_to (number, 可选): 出版年份上限

  • venue_filter (string, 可选): 出版场所的不区分大小写子字符串过滤器

  • include_bibtex (boolean, 可选): 是否在结果中包含 BibTeX 条目。默认为 false

get_author_publications

检索特定作者的出版物详情，支持模糊匹配。

参数：

  • author_name (string, 必需): 完整或部分作者姓名（不区分大小写）

  • similarity_threshold (number, 必需): 0 到 1 之间的浮点数，1.0 表示完全匹配

  • max_results (number, 可选): 返回的最大出版物数量。默认为 20

  • include_bibtex (boolean, 可选): 是否在结果中包含 BibTeX 条目。默认为 false

get_venue_info

检索关于出版场所的详细信息。

参数：

  • venue_name (string, 必需): 出版场所名称或缩写（例如 'ICLR' 或全称）

add_bibtex_entry

将 BibTeX 条目添加到集合中以便稍后导出。

参数：

  • dblp_key (string, 必需): 搜索结果中的 DBLP 键（例如 "conf/nips/VaswaniSPUJGKP17"）

  • citation_key (string, 必需): 在 .bib 文件中使用的引用键（例如 "Vaswani2017"）

行为：

  • 使用提供的键直接从 DBLP 获取 BibTeX 条目

  • 将引用键替换为您自定义的键

  • 添加到会话集合中（重复键会被覆盖）

  • 返回包含集合计数的即时成功/失败反馈

  • 允许重试单个失败的条目

export_bibtex

将所有收集的 BibTeX 条目导出到 .bib 文件。

参数：

  • path (string, 必需): .bib 文件的绝对路径（例如 "/path/to/refs.bib"）

行为：

  • 将通过 add_bibtex_entry 添加的所有条目保存到指定路径

  • 如果缺少 .bib 扩展名，则会自动添加

  • 必要时创建父目录

  • 导出成功后清除集合

  • 返回保存文件的完整路径

  • 如果集合为空，则返回错误

重要提示： BibTeX 条目直接从 DBLP 获取，具有 10 秒超时保护，不会被 LLM 处理、修改或产生幻觉。这确保了书目数据的最高准确性和可信度。仅按指定修改引用键。如果请求超时，将返回错误消息，且该条目不会被添加到集合中。

示例

输入文本：

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.

输出文本：

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.

输出 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}
}

免责声明

此 MCP-DBLP 处于原型阶段，应谨慎使用。鼓励用户进行实验，但在关键环境中使用需自行承担风险。

许可证

本项目采用 MIT 许可证 - 有关详细信息，请参阅 LICENSE 文件。

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

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