Iwac MCP Server

IWAC MCP Server

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

1. Download the latest bundle for your OS using the links above.
2. Double-click the file. Claude Desktop shows an install dialog — click Install.
3. On first use the server downloads ~250 MB of parquet data from Hugging Face into ~/.iwac-mcp/cache/ (override in the extension settings).

The bundle contains the server and DuckDB binaries for your OS (x64 and arm64). Claude Desktop supplies the Node.js runtime, so no separate Node.js or Python installation is needed. We publish desktop bundles for Windows and macOS.

Extension settings and updates

Open Settings → Extensions → Islam West Africa Collection (IWAC) in Claude Desktop to configure these options:

Semantic search and private access are independent options. Using both requires both credentials. The shared hosted endpoint serves public data.

Updating: download and open the latest bundle for your OS to update the extension. If the Hugging Face fields are missing, your installed extension may predate v3.6.0. After updating, review the settings, save any changes, and restart Claude Desktop.

Optional private full-text access

Public data remains the default and needs no token. In the desktop extension, enable Use private full dataset, enter the Hugging Face token (private dataset only), save the settings, and restart. Use a fine-grained token with read access to fmadore/islam-west-africa-collection-full. The token field is marked sensitive. Never paste your token into a chat or commit it.

Other local launchers can set IWAC_PRIVATE_DATASET=true and provide IWAC_HF_TOKEN (or HF_TOKEN). A token alone does not enable private mode; public downloads do not send it. No new dependency or account system is needed.

Private files use a separate private-full/ subdirectory of IWAC_CACHE_DIR (default: ~/.iwac-mcp/cache). Restart after changing modes. Missing tokens and private HTTP 401/403/404 errors fail without cache fallback. Network outages may use that mode's cache. Explicit IWAC_OFFLINE=true uses downloaded files without authentication; removing a token does not erase private files.

Keep the shared hosted endpoint public. This setting applies to the whole instance: everyone who can query a private instance can access its full text.

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 the latest iwac-mcp-skill.zip, 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/.

  Installing it this way is still worth doing: an installed skill is matched against your question automatically, before any tool is called.

The server also serves the skill (skill://)

Every build embeds the research skill as MCP resources, including the Docker image. The Skills extension is finalized (SEP-2640); clients supporting it can discover the same catalogue through skills/list and skills/get under io.modelcontextprotocol/skills. Clients without that extension can use ordinary resources/read:

The optional resources/directory/read method is not advertised. The bare skill://iwac-mcp URI is a catalogue document, not a directory. Skill content is a build-time snapshot; editing the source requires a rebuild. The release zip remains available for clients that install skills locally. Remote clients can read the same workflow without downloading a release artifact.

What it gives Claude

38 possible read-only tools across seven IWAC subsets. 35 work out of the box; the 3 semantic_search_* tools are optional and use Gemini or an explicitly configured local provider (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.

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 13,261 fully-dated articles the archive's rhythm is plain: Ramadan +74%, Dhu al-Hijja +68% (hajj and Tabaski) and Shawwal +42% (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 five models — gpt-5-6-luna (the one the inline columns report), mistral-small-2603, deepseek-v4-flash-0731, gemma-4-31b-it and qwen3-8-27b. All five agree on polarity for only ~32% of articles, so get_sentiment_distribution(model="all") is the honest way to quote a figure. They do not all cover the same articles either — qwen3-8-27b scores 12,098 where the rest score 12,298 — so each model reports its own coverage. model="consensus" returns the panel's precomputed majority (not a sixth model), and search_by_sentiment(disputed=…) reads the articles it split on
• 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
• 1,700+ audiovisual items — francophone web video from Burkina Faso, Togo and Benin (harvested from public channels, still growing, searchable by channel and reachable through a watch URL), plus 47 deposited Nigerian Hausa/Arabic recordings with files — and archival documents

Research workbench

explore_corpus connects selections to sources, keyword contexts, coverage heatmaps, comparisons, publication-country/mentioned-place matrices, and pageable manifest, CSL-JSON and BibTeX exports. iwac://datasets/{subset} resources expose current columns, field availability and dataset provenance. Temporal charts support normalized shares with explicit denominators. Sentiment comparisons accept the same selection filters and a chosen pair of models, with Cohen’s kappa and quadratic weighted kappa on explicitly reported populations. Exact chart selections, source reading, Back navigation and provenance exports are shared across the app. See the workbench guide for examples, interpretation limits, cache behavior and local embedding migration.

On supported MCP Apps hosts, Fullscreen expands the view, and a compact summary of the current selection is shared automatically with the assistant. Ask about this selection sends an explicit question with that selection's snapshot; automatic updates do not start a conversation turn. The summary carries bounded filters, counts, source IDs and provenance, with omissions marked; full source text still requires retrieval. Controls depend on the host's advertised capabilities. See the interaction contract.

Architecture

• Data: parquet files from the IWAC Hugging Face dataset are lazily downloaded per subset (articles, publications, documents, audiovisual, images, index, references) into a local cache and queried through DuckDB views. A long-running server re-checks each subset daily (IWAC_REFRESH_HOURS) and swaps in a newer revision without a restart. Each request pins its files; old generations are retained for other readers and reproducibility. All SQL is parameterised; matching is accent/case-insensitive.
• Transports: stdio (the default — what the Claude Desktop .mcpb uses), and a stateless Streamable-HTTP mode (node server/index.js --http) behind a bearer token, which the Docker image runs for the hosted https://islam.zmo.de/mcp/ endpoint.
• Docker: every release publishes ghcr.io/fmadore/iwac-mcp-server for self-hosting the HTTP endpoint — see mcpb/README.md for the required env vars and token setup.

Develop

This implementation requires Node.js 24 or newer for both the build and server runtime. Node.js 20 is end of life. Desktop hosts with an older embedded runtime must be upgraded before installing the next bundle; the manifest rejects incompatible runtimes.

The bundle lives under mcpb/. See mcpb/README.md for the build / pack workflow.

cd mcpb
npm ci
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, fixture, app, skills, HTTP and token-budget tests
npm run test:browser # Chromium app behavior (install browser first)
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, checks that the version is unpublished, packs and validates desktop bundles on macOS/Windows, and smoke-tests the Docker image before publication. The publish job uses those tested artifacts. Existing releases and registry versions cannot be overwritten; use a new version for a new release.

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.

How to cite

Machine-readable metadata lives in CITATION.cff — GitHub's Cite this repository button (sidebar) renders it as APA or BibTeX with the current version filled in. In text:

Madore, F. (2026). IWAC MCP Server (Version 3.8.0) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.21805837

@software{madore_iwac_mcp_server,
  author    = {Madore, Frédérick},
  title     = {{IWAC MCP Server}},
  year      = {2026},
  version   = {3.8.0},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.21805837},
  url       = {https://github.com/fmadore/iwac-mcp-server},
  license   = {MIT}
}

That DOI is the concept DOI — it always resolves to the newest release, so it stays correct as versions come and go. If you need to cite the exact version you ran, take the per-version DOI from the Zenodo record.

If the software helped you reach a finding, please cite the collection itself as well — that is where the archival work lives.

License

MIT

Related

• IWAC Hugging Face Dataset
• IWAC Digital Archive
• Desktop Extensions spec (MCPB)