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.

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 bundle for your OS from Releases.
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).

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

27 possible read-only tools across six IWAC subsets. 25 work out of the box; the 2 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 and the stats tools also return MCP structured content (outputSchema + structuredContent), which the ChatGPT connector contract requires.

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 Gemini sentiment analysis (polarity / centrality / subjectivity)
• 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

Develop

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

cd mcpb
npm install
node scripts/install-duckdb-bindings.mjs
npm run typecheck   # tsc --noEmit
npm run build       # esbuild -> single server/index.js
npm test            # unit tests + offline fixture MCP round-trip (no network)
npm run test:live   # full smoke test against the real HF dataset (~250 MB)

CI runs the typecheck, build, unit tests, and the offline fixture test on every push; the live smoke test runs weekly (its pinned counts are the dataset-drift alarm).

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

MIT

Related

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