wikicitation-mcp
Provides 40 tools to Claude for Wikipedia citation analysis, edit history, and DOI/ISBN annotation, enabling citation quality scoring and metadata enrichment without leaving the chat.
README
wikicitation-mcp
Wikipedia citation-science tools for Claude — pure Python, no R required.
An MCP server that exposes 40 tools to
Claude Code, Claude Desktop, and claude.ai.
Ask Claude to fetch Wikipedia edit histories, analyse citations, score
scientific quality, and annotate DOIs and ISBNs — all without leaving the chat.
What you can do
| Category | Examples |
|---|---|
| Edit history | Full revision history, first/latest wikitext, category-wide sweeps |
| Citation analysis | Count DOIs/refs/ISBNs, extract CS1 templates, compute SciScore |
| DOI & ISBN annotation | Enrich DOIs via CrossRef / EuropePMC / Altmetric; books via Google Books / Open Library |
Installation
Option A — PyPI (recommended)
No clone needed. uvx downloads and runs the package in one step:
# Requires uv (https://docs.astral.sh/uv/)
uvx wikicitation-mcp
Or install permanently with pip:
pip install wikicitation-mcp
wikicitation-mcp # starts the stdio server
Option B — From source
git clone https://github.com/jsobel1/wikicitation-mcp.git
cd wikicitation-mcp
uv sync
uv run python server.py
Connect to Claude
Claude Code (CLI) — PyPI
claude mcp add wikilite -- uvx wikicitation-mcp
claude mcp list
Claude Code (CLI) — from source
# Run from inside the cloned directory
claude mcp add wikilite -- uv run python server.py
Claude Desktop — PyPI
Edit ~/.claude/claude_desktop_config.json
(Windows: %APPDATA%\Claude\claude_desktop_config.json):
{
"mcpServers": {
"wikilite": {
"command": "uvx",
"args": ["wikicitation-mcp"]
}
}
}
Claude Desktop — from source
{
"mcpServers": {
"wikilite": {
"command": "uv",
"args": [
"run", "python",
"/ABSOLUTE/PATH/TO/wikicitation-mcp/server.py"
],
"cwd": "/ABSOLUTE/PATH/TO/wikicitation-mcp"
}
}
}
Restart Claude Desktop — the wikilite server appears in the toolbar.
claude.ai (web, HTTP transport)
uvx wikicitation-mcp --transport streamable-http --port 8000
# Then in claude.ai → Settings → Connections → Add MCP server
# URL: http://localhost:8000/mcp
Quick start
Once connected, try these prompts in Claude:
Fetch all edits to the Wikipedia article "Circadian clock" up to today.
Count all DOIs, references, and ISBNs in this wikitext: [paste wikitext]
Calculate the SciScore for "Sleep deprivation".
Extract all citations from "CRISPR" — how many are journals vs websites vs books?
Show me the top 20 most-cited papers in the Wikipedia article "mRNA vaccine".
Annotate these DOIs with CrossRef metadata:
10.1038/nature12373, 10.1016/j.cell.2020.01.001
Usage notes
date_limit is an upper bound
date_limit controls the newest revision returned, not the oldest.
To retrieve edits "since 2023", pass today's date as the limit and filter
the returned rows by timestamp.
SciScore
get_sci_score returns two metrics:
| Field | Formula | Meaning |
|---|---|---|
sci_score |
journal citations ÷ total CS1 citations | Proportion of references that are peer-reviewed journal articles |
Range 0 to 1; higher = more scientifically sourced.
Altmetric
Altmetric returns attention scores without an API key for most DOIs and ISBNs.
If a DOI or ISBN is not indexed, altmetric_score will be null.
For production use, set the ALTMETRIC_KEY environment variable.
Tool reference
Group 1 — History & metadata (14 tools)
| Tool | Key arguments | Returns |
|---|---|---|
get_article_history |
article_name, date_limit |
revision table (revid, user, timestamp, size, comment) |
get_article_recent |
article_name, date_limit |
metadata + full wikitext |
get_article_initial |
article_name |
first-ever revision metadata + wikitext |
get_article_info |
article_name |
pageid, title, byte length |
get_tables_all |
article_name, date_limit |
initial + recent + history + info in one call |
get_category_pages |
category |
list of article titles |
get_pages_in_cat_table |
category |
structured page table (pageid, title, type) |
get_subcat_table |
catname, replacement |
direct subcategories |
get_subcat_multiple |
catname_list |
subcategories for N categories |
get_subcat_with_depth |
catname, depth, replacement |
recursive subcategories |
get_page_in_cat_multiple |
catname_list |
pages for N categories |
get_category_history |
article_list |
revision table for a list of articles |
get_category_recent |
article_list, date_limit |
metadata + wikitext for a list of articles |
get_category_creation |
article_list |
creation-revision metadata for a list of articles |
Group 2 — Citation counting, extraction & quality (19 tools)
| Tool | Key arguments | Returns |
|---|---|---|
get_doi_count |
text |
{"count": N} |
get_ref_count |
text |
{"count": N} |
get_url_count |
text |
{"count": N} |
get_isbn_count |
text |
{"count": N} |
get_hyperlink_count |
text |
{"count": N} |
get_any_count |
text, regexp |
{"count": N} |
extract_citations |
text |
list of CS1 template strings |
extract_wikihypelinks |
text |
list of [[...]] wikilink strings |
replace_wikihypelinks |
text |
{"cleaned_text": ...} with links replaced by display text |
parse_cite_type |
text |
{"cite_type": ..., "fields": {...}} |
extract_with_regex |
article_name, regexp, date_limit |
list of regex matches from live article |
extract_all_regex |
article_name, date_limit |
all built-in pattern matches (doi, url, isbn, pmid, …) |
parse_citations |
article_name, date_limit |
structured citation rows (cite_type, doi, author, year, title) |
parse_all_citations |
article_name, date_limit |
long-form table: one row per citation field |
get_citation_types |
article_name, date_limit |
citation counts by CS1 type |
get_source_type_counts |
text |
citation counts by CS1 type from raw wikitext |
get_sci_score |
article_name, date_limit |
sci_score (see above) |
get_top_cited_papers |
article_name, date_limit |
top 40 DOIs annotated via EuropePMC |
get_revert_counts |
start, end |
revert-tagged edit counts by article (≤30-day window) |
Group 3 — DOI & ISBN annotation (7 tools)
| Tool | Key arguments | Returns |
|---|---|---|
annotate_dois_europmc |
doi_list |
title, journal, pubYear, citedByCount, isOpenAccess, … |
annotate_dois_crossref |
doi_list |
title, authors, journal, year, publisher, citation count |
annotate_dois_altmetric |
doi_list |
Altmetric score, tweet count, news mentions, … |
annotate_dois_bibtex |
doi_list |
{"bibtex_entries": [...]} |
annotate_isbn_google |
isbn |
title, authors, publisher, publishedDate, description |
annotate_isbn_openlib |
isbn |
title, authors, publishers, publish_date, page count |
annotate_isbns_altmetric |
isbn_list |
Altmetric score per ISBN |
Running tests
# Unit tests — no network required
uv run pytest tests/ -m "not integration" -v
# Integration tests — require internet
uv run pytest tests/ -m integration -v
# Full suite
uv run pytest tests/ -v
Troubleshooting
uvx: command not found
Install uv first:
# macOS / Linux
curl -LsSf https://astral.sh/uv/install.sh | sh
# Windows (PowerShell)
powershell -ExecutionPolicy Bypass -c "irm https://astral.sh/uv/install.ps1 | iex"
Server not appearing in Claude Code
claude mcp remove wikilite
claude mcp add wikilite -- uvx wikicitation-mcp
claude mcp list
ModuleNotFoundError: No module named 'mwparserfromhell'
The venv is missing a dependency. Run uv sync inside the cloned repo, or
reinstall via pip install wikicitation-mcp.
get_revert_counts returns no results
The Wikipedia recentchanges API only retains ~30 days of history. Requests
for date ranges older than that will return empty results.
Related projects
These three projects share the same core analysis logic at three different levels of interactivity — pick the one that matches how you want to work.
| Project | Interface | When to use |
|---|---|---|
| wikicitation-mcp (this repo) | MCP server for Claude Desktop / Claude Code / claude.ai | You want to ask Claude questions in natural language and have it call the tools for you. Pure Python, no R required. |
| wikilite | R package | You want a programmable pipeline in R / R Markdown / Quarto with full access to revision tables, citation parsers, and ggplot2 / plotly / visNetwork output. |
| wikiliteApp | Shiny web app (built on wikilite) |
You want a point-and-click GUI: per-article tabs (History Flow, Citations, Authorship, Stability with SciScore-over-time, Vandalism & Wars, WikiWho-powered Revision Inspector) and a Corpus Analysis mode (multi-corpus timeline, per-article + per-corpus SciScore, co-citation / publication / wikilink networks, batched EuropePMC + Google Books annotation, multi-sheet XLSX exports). |
External references
- Model Context Protocol — MCP specification
- FastMCP — Python MCP framework used here
- mwparserfromhell — wikitext parser
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.