Iwac
FreeNot checkedMCP server providing AI assistants with structured access to the Islam West Africa Collection (IWAC) — 12,000+ newspaper articles, sentiment analysis, and index
About
MCP server providing AI assistants with structured access to the Islam West Africa Collection (IWAC) — 12,000+ newspaper articles, sentiment analysis, and index data from six West African countries.
README
A read-only Model Context Protocol server for the
Islam West Africa Collection (IWAC).
Ships as a one-click Desktop Extension
(.mcpb) for Claude Desktop, backed by the
IWAC Hugging Face dataset.
Also available as a hosted endpoint at https://islam.zmo.de/mcp/ for ChatGPT
and other MCP clients — see docs/connecting.md for the
full connection walkthrough (Claude Desktop and ChatGPT).
Install
Each release ships a
server bundle for your operating system plus a research-skill .zip. The
.mcpb gives Claude the data and tools; the .zip adds a research skill that
teaches Claude how to use them. Install the server first, then install the
skill too — strongly recommended for getting the most out of the tools: it
makes Claude search and synthesize far more efficiently, with fewer wasted
queries.
1. The MCP server — pick the bundle for your OS
| Your OS | Download |
|---|---|
| Windows (Intel/AMD or Snapdragon) | iwac-mcp-server-windows.mcpb |
| macOS (Apple Silicon or Intel) | iwac-mcp-server-macos.mcpb |
- Download the bundle for your OS from Releases.
- Double-click the file. Claude Desktop shows an install dialog — click Install.
- On first use the server downloads ~250 MB of parquet data from Hugging Face
into
~/.iwac-mcp/cache/(override in the extension settings).
No Python, no uv, no venv — the bundle ships a self-contained Node runtime and
the DuckDB binaries for your OS (x64 and arm64; Claude Desktop picks the right
one). Claude Desktop has no Linux build, so no Linux bundle is published.
2. The research skill — iwac-mcp-skill.zip (strongly recommended)
The iwac-mcp skill wraps the raw tools in a structured research workflow: a five-phase methodology, francophone search strategy, source attribution with confidence grading, and bias/coverage caveats. It makes the server far more efficient to use — Claude picks the right tool and search terms on the first pass (fewer wasted queries), searches French sources properly, and returns a cited synthesis instead of a raw tool dump. You can run the tools without it, but you'll get more out of every query with it installed.
Download iwac-mcp-skill.zip from the same release, then:
Claude Desktop — open Customize → Skills → + → Create skill → Upload a skill and select the zip. (Or unzip it into
~/.claude/skills/and restart Claude Desktop.)Claude Code — unzip it into your skills directory; Claude Code discovers it live, no restart needed:
# macOS / Linux unzip iwac-mcp-skill.zip -d ~/.claude/skills/# Windows (PowerShell) Expand-Archive iwac-mcp-skill.zip -DestinationPath $HOME\.claude\skills\Both land the skill at
~/.claude/skills/iwac-mcp/. The repository source of truth is.agents/skills/iwac-mcp/; keep project-local copies there rather than duplicating the same skill under.claude/.
What it gives Claude
37 possible read-only tools across seven IWAC subsets. 34 work out of the
box; the 3 semantic_search_* tools are optional and require a free
Google/Gemini API key (disabled by default). All keyword and filter matching is
accent- and case-insensitive. The unified search/fetch pair, the stats
tools, the aggregates, list_periodicals, and get_sentiment_distribution also
return MCP structured content (outputSchema + structuredContent), which the
ChatGPT connector contract requires.
| Group | Tools |
|---|---|
| Cross-subset | search, fetch |
| Articles | search_articles, get_article, semantic_search_articles |
| Sentiment | search_by_sentiment, get_sentiment_distribution |
| Index | search_index, get_index_entry, list_subjects, list_locations, list_persons |
| Stats | get_collection_stats, get_newspaper_stats, get_country_comparison, get_temporal_distribution |
| Aggregates | get_topic_distribution, get_field_distribution, get_cooccurrence, get_lexical_metrics, get_place_distribution, get_semantic_map, get_similar_items |
| Publications | search_publications, list_periodicals, get_publication_fulltext, semantic_search_publications |
| References | search_references, get_reference |
| Images | search_images, get_image, semantic_search_images |
| Other | search_documents, get_document, search_audiovisual, list_audiovisual, get_audiovisual |
The aggregates answer questions about a whole set rather than returning its items: how it spreads across the 30 precomputed LDA topics, which subjects, places or bylines dominate it, what gets discussed alongside what, how its prose reads, where on a map it points, how it lays out in embedding space, and what a given item's nearest neighbours are. Eleven tools in all — the stats family plus these — declare an MCP App view, so in Claude they render as interactive charts rather than JSON.
get_temporal_distribution also reads the Islamic calendar. With
granularity="lunar_month" it pools every year into the twelve lunar months —
the one bucket a Gregorian axis structurally cannot produce, because the Hijri
year drifts ~11 days annually and so smears each observance across all twelve
Gregorian months. Over the 12,220 fully-dated articles the archive's rhythm is
plain: Ramadan +72%, Dhu al-Hijja +70% (hajj and Tabaski) and Shawwal +44%
(Korité) against an even split, while Rabi' I — Maouloud — sits flat. search_articles
and search_publications take hijri_month (1–12 or a name in either
transliteration) and hijri_year to read the items behind a peak. The lunar
dates are precomputed in the dataset pipeline with the Umm al-Qura tables, the
same converter the on-this-day block on islam.zmo.de uses, so the two never
disagree; items dated only to a year or month have no lunar date and are reported
in imprecise_date_count rather than plotted.
The three full-text tools — get_article, get_document, and
get_publication_fulltext — optionally take a keyword to return ~2000-char
excerpts around each match, so Claude reads just the relevant passages of a long
article, archival document, or periodical issue instead of the whole OCR.
Every result object includes a url field pointing at the canonical IWAC record,
e.g. https://islam.zmo.de/s/afrique_ouest/item/28576.
About the collection
IWAC is a digital archive focused on Islam and Muslims in West Africa:
- 12,000+ newspaper articles from Benin, Burkina Faso, Côte d'Ivoire, Niger,
and Togo, 1960s–present (mostly French), each with an AI abstract and AI
sentiment analysis (polarity / centrality / subjectivity), scored
independently by
gemini-3-flash-preview,gpt-5-miniandministral-14b-2512 - 4,700+ authority records (persons, organisations, places, events, subjects)
- 1,500+ Islamic publications (periodical issues, books) with full OCR
- 860+ academic references, half with abstracts
- Archival documents and Nigerian audiovisual materials
Architecture
- Data: parquet files from the IWAC Hugging Face dataset are lazily downloaded per subset (articles, publications, documents, audiovisual, index, references) into a local cache and queried through DuckDB views. All SQL is parameterised; matching is accent/case-insensitive.
- Transports: stdio (the default — what the Claude Desktop
.mcpbuses), and a stateless Streamable-HTTP mode (node server/index.js --http) behind a bearer token, which the Docker image runs for the hostedhttps://islam.zmo.de/mcp/endpoint. - Docker: every release publishes
ghcr.io/fmadore/iwac-mcp-serverfor self-hosting the HTTP endpoint — see mcpb/README.md for the required env vars and token setup.
Develop
The bundle lives under mcpb/. See mcpb/README.md for the build / pack workflow.
cd mcpb
npm install
npm run install-bindings # fetch the 4 macOS/Windows DuckDB binaries
npm run typecheck # tsc --noEmit
npm run lint # biome (linter only)
npm run build # esbuild -> single server/index.js
npm test # unit tests + offline fixture & HTTP MCP round-trips (no network)
npm run test:live # full smoke test against the real HF dataset (~250 MB)
CI runs the version check, typecheck, lint, build, unit tests, and the offline
fixture + HTTP round-trip tests on every push to main and every pull request;
the live smoke test runs weekly (its pinned counts are the dataset-drift alarm).
Releases: push a v* tag — the release workflow re-runs the full test suite,
packs the per-OS .mcpb bundles and skill zip, smoke-tests and pushes the
Docker image, uploads the release assets, and publishes to the MCP Registry.
Roadmap
See TODO.md — near-term: submit to the Anthropic extension directory, sign the bundle with a production code-signing cert, and replace Gemini semantic-search with a free local model.
License
Related
Installing Iwac
This server has no published package — it is built from source. Open the repository and follow its README.
▸ github.com/fmadore/iwac-mcp-serverFAQ
Is Iwac MCP free?
Yes, Iwac MCP is free — one-click install via Unyly at no cost.
Does Iwac need an API key?
No, Iwac runs without API keys or environment variables.
Is Iwac hosted or self-hosted?
Self-hosted: the server runs locally on your machine via the install command above.
How do I install Iwac in Claude Desktop, Claude Code or Cursor?
Open Iwac on unyly.org, pick your client tab (Claude Desktop, Claude Code, Cursor) and press Install — the config is generated automatically, no JSON editing.
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