by szeider
MCP-DBLP is a Model Context Protocol (MCP) server that provides Large Language Models (LLMs) with access to the DBLP computer science bibliography database, enabling AI models to interact with and retrieve academic publication data.
MCP-DBLP is a Model Context Protocol (MCP) server that provides Large Language Models (LLMs) with access to the DBLP computer science bibliography database. It acts as a bridge, allowing AI models to interact with and retrieve academic publication data from DBLP.
To use MCP-DBLP, you need to:
uv venv
and uv pip install -e .
.claude_desktop_config.json
for Claude Desktop) in the appropriate directory for your operating system and add the MCP-DBLP server command and arguments, including the absolute path to the mcp-dblp
directory and a BibTeX export folder.Once set up, you can use an instructions prompt (like the provided instructions_prompt.md
) with your text containing citations to leverage MCP-DBLP's functionalities.
MCP-DBLP offers a range of features to facilitate interaction with the DBLP database:
search
: Search DBLP 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.MCP-DBLP enables AI models to:
Q: Is MCP-DBLP ready for production use? A: No, MCP-DBLP is currently in its prototype stage and should be used with caution. Any use in critical environments is at your own risk.
Q: What are the system requirements for MCP-DBLP?
A: You need Python 3.11+ and uv
(a Python package installer and resolver).
Q: How does the export_bibtex
tool ensure accuracy?
A: The export_bibtex
tool fetches BibTeX entries directly from DBLP with a timeout protection. It does not process, modify, or hallucinate the data through the LLM, ensuring maximum accuracy and trustworthiness. Only the citation keys are modified as specified.
A Model Context Protocol (MCP) server that provides access to the DBLP computer science bibliography database for Large Language Models.
The MCP-DBLP integrates the DBLP (Digital Bibliography & Library Project) API with LLMs through the Model Context Protocol, enabling AI models to:
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 |
Provide feedback to the author via this form.
Install an MCP-compatible client (e.g., Claude Desktop app)
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 .
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
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.
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 10year_from
(number, optional): Lower bound for publication yearyear_to
(number, optional): Upper bound for publication yearvenue_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 falseSearch 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 matchmax_results
(number, optional): Maximum number of publications to return. Default is 10year_from
(number, optional): Lower bound for publication yearyear_to
(number, optional): Upper bound for publication yearvenue_filter
(string, optional): Case-insensitive substring filter for publication venuesinclude_bibtex
(boolean, optional): Whether to include BibTeX entries in the results. Default is falseRetrieve 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 matchmax_results
(number, optional): Maximum number of publications to return. Default is 20include_bibtex
(boolean, optional): Whether to include BibTeX entries in the results. Default is falseRetrieve detailed information about a publication venue.
Parameters:
venue_name
(string, required): Venue name or abbreviation (e.g., 'ICLR' or full name)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 entries directly from DBLP to a local file.
Parameters:
links
(string, required): HTML string containing one or more key links
"<a href=https://dblp.org/rec/journals/example.bib>Smith2023</a>"
Behavior:
--exportdir
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.
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.
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.
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}
}
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.
This project is licensed under the MIT License - see the LICENSE file for details.
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