MCP-DBLP

A Model Context Protocol (MCP) server that provides access to the DBLP computer science bibliography database for Large Language Models (accompanying paper accepted to AI4SC @ AAAI-26).

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

Related MCP server: YDB MCP

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

Feedback

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System Requirements

• Python 3.11+

• uv

Installation

Claude Code

Simply run:

Claude Desktop

Add to your Claude Desktop configuration file:

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

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

From Source (Development)

Then configure Claude Desktop with:

Instructions

Usage instructions are available via the get_instructions tool. Key workflow points are shown in the tool description; call the tool for complete details. See also instructions_prompt.md.

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'

add_bibtex_entry

Add a BibTeX entry to the collection for later export.

Parameters:

• dblp_key (string, required): The DBLP key from search results (e.g., "conf/nips/VaswaniSPUJGKP17")

• citation_key (string, required): The citation key to use in the .bib file (e.g., "Vaswani2017")

Behavior:

• Fetches BibTeX entry directly from DBLP using the provided key

• Replaces the citation key with your custom key

• Adds to session collection (duplicate keys are overwritten)

• Returns immediate success/failure feedback with collection count

• Allows retry of individual failed entries

export_bibtex

Export all collected BibTeX entries to a .bib file.

Parameters:

• path (string, required): Absolute path for the .bib file (e.g., "/path/to/refs.bib")

Behavior:

• Saves all entries added via add_bibtex_entry to the specified path

• The .bib extension is added automatically if missing

• Parent directories are created if needed

• Clears the collection after successful export

• Returns the full path to the saved file

• Returns error if collection is empty

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 returned and the entry is not added to the collection.

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

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.

License

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