An MCP server connects an AI assistant to one outside system. Like an app to your accounts:
GmailGoogle CalendarGoogle DriveSlackGitHub→IWAC
RAG
Retrieval-augmented generation. Proven for GLAM, but it chunks records, and retrieves by a similarity the user never sees.
MCP
Ingests nothing. The data stays put; the server holds the logic; the model just asks and answers. modelcontextprotocol.io
The analogy
Like IIIF for images: a shared interface, without handing over the originals. Build the server once — Claude, ChatGPT, or open-source models all connect.
Agent skills
What is a skill.md?
A skill is a methods handbook for a machine reader — loaded only when a request matches its purpose.
Competence by reading, not retraining
Versioned, reviewed, refined — a prompt is typed once and lost
An open standard across agents — no vendor lock-in, like MCP
Born in coding agents — the format fits any domain
The standard layout of an agent skill — SKILL.md is the only required file.
What you can ask it
The IWAC MCP server
"Newspaper articles on Islam in Abidjan, in the 1990s"articlesplace · period
"Islamic publications on secularism in Togo and Benin"publicationssubject · country
"Academic references on Muslim women in Burkina Faso"referencessubject · country
One plain-language request; the model picks the tools and combines the filters.
22 tools · 6 subsets
Read-only: search, full text, authority records, sentiment, stats
Verifiable: every call a lookup, no AI at query time
Traceable: every result links to its canonical record
Two ways in: one-click for Claude Desktop, a hosted URL for ChatGPT
Inside the server · semantic search
Search by meaning, not words
1 · AskYour question
« Islam et laïcité au Burkina Faso »
Plain language — and any language works.
→
2 · EmbedTurned into coordinates
An embedding model (Gemini) maps the text to 768 numbers — one point in a space of meaning.
laïcité → [0.07, -0.12, 0.93, …]
Every article was mapped the same way, in advance.
→
3 · MatchNearest points win
Cosine similarity ranks all 12,000+ articles by how close they sit — closest in meaning, not in wording.
Why it matters
Finds the right articles even when they never say « laïcité » — secularism, Franco-Arabic schools, family-code debates. The one tool that runs a model live, just to turn your question into coordinates.
Search the collection · offline demo
Keyword vs. meaning — in the slide itself
laïcité au Burkina Faso
2 documents · exact-string match only
« … le débat sur la laïcité au Burkina Faso … »
L’Observateur Paalga · 1994
« … la laïcité de l’État et les écoles … »
Sidwaya · 2002
The blind spot
Dozens of articles are about secularism but never type the word — schooling, family-code debates, the Arabic press. Keyword search cannot see them. Click By meaning.
7 documents · ranked by meaning · across languagescosine similarity
Écoles franco-arabes et le programme nationaleducation · state0.89
Le code de la famille relance la controversefamily law0.86
L’État et les associations islamiquesreligion · state0.84
النظام العلماني ودوره في التعليمArabic · cross-lang0.81
Neutralité religieuse de l’administration publiquegovernance0.79
None contain the word laïcité — all are about the idea, including the Arabic record keyword search would never reach. · illustrative slice
The IWAC skill in motion
A five-phase research method
Two depths, chosen up front:
Briefdefaulta quick scan and a few close reads
Extendedthe full five-phase analysis below
Scoping — what does the collection even hold for this question, before any keyword?
Systematic searching — French + transliteration variants (Tabaski = Eid al-Adha); log every search, including null results
Deep reading — full text and sentiment, article by article
Triangulation — cross-check articles, publications, references, index entries
Synthesis — findings with source attribution and confidence grades
The point
Twenty-two tools give reach. The "judgement" lives in the skill.
What a server never could
Discipline, disclosure — and a new role
Linguistic discipline — search the French corpus in French, whatever language the question is in
Bias disclosure — every synthesis states the skew: francophone tilt, missing Arabisants, thin Niger and Nigeria
Evidential discipline — claims tagged primary / secondary / AI-derived; absence of evidence ≠ evidence of absence
The role shift
Cataloguer, then API designer — now author of research methods a machine can follow. The server took a weekend; the skill is where the expertise lives.
The same question we put to Gemini earlier, now answered from the IWAC.
The stakes
Whose infrastructure? African collections and AI extraction
39 of 43 heritage institutions hit by AI-bot traffic spikes (Weinberg, GLAM-E Lab, 2025); Wikimedia: 65% of its costliest traffic is bots. Read it ↗
The default is extraction — public institutions and open-access labour subsidising commercial AI
MCP, a third path — the collection stays put; the institution decides what the AI sees
Yékú's paradox · 2026
Un-digitised, an African archive is invisible to AI; digitised, it's exposed to scraping — the incomplete corpus becomes the single story, the colonial library rebuilt in the training set. Read the essay ↗
Conclusion
A new way in for a wider public — but no replacement for archival research. It finds what you ask for, not what you weren’t looking for: the unexpected find that slow reading turns up.
Try it: connect Claude or ChatGPT to the collection — how-to on the IWAC AI-access page.
Next: a small open-source model on the server itself, answering on the website — no install, no account. github.com/fmadore/iwac-mcp-server