Mcp Dblp

Mcp Dblp

What is Mcp Dblp about?

Mcp Dblp integrates the DBLP API with the Model Context Protocol, allowing large language models to query academic publications, retrieve BibTeX entries, perform fuzzy matching on titles and author names, filter by year or venue, and generate statistics on result sets.

How to use Mcp Dblp?

1. Install the server

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

2. Configure the client (e.g., Claude Desktop) by adding a server entry to claude_desktop_config.json:

   {
     "mcpServers": {
       "mcp-dblp": {
         "command": "uv",
         "args": [
           "--directory",
           "/absolute/path/to/mcp-dblp/",
           "run",
           "mcp-dblp",
           "--exportdir",
           "/absolute/path/to/bibtex/export/folder/"
         ]
       }
     }
   }
   

3. Invoke tools from an MCP‑compatible client using the defined tool names (search, fuzzy_title_search, get_author_publications, get_venue_info, calculate_statistics, export_bibtex).

Key Features

• Boolean and fuzzy search across the entire DBLP collection
• Direct BibTeX retrieval with optional export to a local .bib file
• Year and venue filtering
• Statistical analysis of result sets (counts per year, venue distribution, etc.)
• Author‑centric queries with fuzzy name matching
• Minimal latency thanks to direct DBLP fetching (no LLM hallucination)

Use Cases

• Academic writing assistance – automatically replace in‑text citations with properly formatted \cite{} commands and generate a bibliography file.
• Research literature reviews – fetch and aggregate publications for a given topic, author, or venue and compute publication trends.
• Citation management tools – integrate with LLM‑driven editors to keep reference lists up‑to‑date.
• Educational platforms – provide students with searchable access to peer‑reviewed computer‑science papers.

FAQ

• Q: Do I need an API key for DBLP? A: No. DBLP is openly accessible; the server communicates directly with the public DBLP endpoints.
• Q: Which Python versions are supported? A: Python 3.11 or newer.
• Q: Can I use the server on Windows? A: Yes. Follow the same installation steps; adjust the path to claude_desktop_config.json to %APPDATA%\Claude\claude_desktop_config.json.
• Q: How are BibTeX keys generated? A: Keys are taken from DBLP entries; the export_bibtex tool can replace them with custom keys supplied in the HTML link text.
• Q: What happens if a DBLP request times out? A: The tool returns an error message for the affected entry while still delivering any successfully fetched records.

MCP-DBLP

A Model Context Protocol (MCP) server that provides access to the DBLP computer science bibliography database for Large Language Models.

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Overview

The MCP-DBLP integrates the DBLP (Digital Bibliography & Library Project) API with LLMs through the Model Context Protocol, enabling AI models to:

• Search and retrieve academic publications from the DBLP database
• Process citations and generate BibTeX entries
• Perform fuzzy matching on publication titles and author names
• Extract and format bibliographic information
• Process embedded references in documents
• Direct BibTeX export that bypasses LLM processing for maximum accuracy

Features

• Comprehensive search capabilities with boolean queries
• Fuzzy title and author name matching
• BibTeX entry retrieval directly from DBLP
• Publication filtering by year and venue
• Statistical analysis of publication data
• Direct BibTeX export capability that bypasses LLM processing for maximum accuracy

Available Tools

Tool Name	Description
search	Search DBLP for publications using boolean queries
fuzzy_title_search	Search publications with fuzzy title matching
get_author_publications	Retrieve publications for a specific author
get_venue_info	Get detailed information about a publication venue
calculate_statistics	Generate statistics from publication results
export_bibtex	Export BibTeX entries directly from DBLP to files

Feedback

Provide feedback to the author via this form.

System Requirements

• Python 3.11+
• uv

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Installation

1. Install an MCP-compatible client (e.g., Claude Desktop app)

2. Install the MCP-DBLP:

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

3. Create the configuration file:

   For macOS/Linux:

   ~/Library/Application/Support/Claude/claude_desktop_config.json

For Windows:

   %APPDATA%\Claude\claude_desktop_config.json

Add the following content:

   {
     "mcpServers": {
       "mcp-dblp": {
         "command": "uv",
         "args": [
           "--directory",
           "/absolute/path/to/mcp-dblp/",
           "run",
           "mcp-dblp",
           "--exportdir",
           "/absolute/path/to/bibtex/export/folder/"
         ]
       }
     }
   }

Windows: C:\\absolute\\path\\to\\mcp-dblp

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Prompt

Included is an instructions prompt which should be used together with the text containing citations. On Claude Desktop, the instructions prompt is available via the electrical plug icon.

Tool Details

search

Search DBLP for publications using a boolean query string.

Parameters:

• query (string, required): A query string that may include boolean operators 'and' and 'or' (case-insensitive)
• max_results (number, optional): Maximum number of publications to return. Default is 10
• year_from (number, optional): Lower bound for publication year
• year_to (number, optional): Upper bound for publication year
• venue_filter (string, optional): Case-insensitive substring filter for publication venues (e.g., 'iclr')
• include_bibtex (boolean, optional): Whether to include BibTeX entries in the results. Default is false

fuzzy_title_search

Search DBLP for publications with fuzzy title matching.

Parameters:

• title (string, required): Full or partial title of the publication (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 10
• year_from (number, optional): Lower bound for publication year
• year_to (number, optional): Upper bound for publication year
• venue_filter (string, optional): Case-insensitive substring filter for publication venues
• include_bibtex (boolean, optional): Whether to include BibTeX entries in the results. Default is false

get_author_publications

Retrieve publication details for a specific author with fuzzy matching.

Parameters:

• 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

get_venue_info

Retrieve detailed information about a publication venue.

Parameters:

• venue_name (string, required): Venue name or abbreviation (e.g., 'ICLR' or full name)

calculate_statistics

Calculate statistics from a list of publication results.

Parameters:

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

export_bibtex

Export BibTeX entries directly from DBLP to a local file.

Parameters:

• links
  

  (string, required): HTML string containing one or more key links

  • Example: "<a href=https://dblp.org/rec/journals/example.bib>Smith2023</a>"

Behavior:

• For each link, the BibTeX entry is fetched directly from DBLP
• Only the citation key is replaced with the key specified in the link text
• All entries are saved to a timestamped .bib file in the folder specified by --exportdir
• Returns the full path to the saved file

Important Note: The BibTeX entries are fetched directly from DBLP with a 10-second timeout protection and are not processed, modified, or hallucinated by the LLM. This ensures maximum accuracy and trustworthiness of the bibliographic data. Only the citation keys are modified as specified. If a request times out, an error message is included in the output.

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Example

Input text:

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.

Output text:

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.

Output 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}
}

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Disclaimer

This MCP-DBLP is in its prototype stage and should be used with caution. Users are encouraged to experiment, but any use in critical environments is at their own risk.

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License

This project is licensed under the MIT License - see the LICENSE file for details.

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