Seshat MCP Server

Seshat MCP Server

Enables querying 10,000 years of historical social-complexity data from the Seshat Global History Databank through 9 tools, allowing counterfactual analysis and comparison of polities in natural language.

Category
Visit Server

README

Seshat — Echoes of History

Ask counterfactual questions of the historical record. Seshat turns the Seshat Global History Databank into something you can interrogate — both programmatically through an MCP server and visually through a web app — and then project what might have happened if history had gone differently.

"What if the Roman Empire had developed gunpowder weapons in 200 BCE? How might its social complexity have evolved?"

This monorepo has two front doors:

  • 🔌 MCP server — exposes 9 Seshat tools to Claude and other MCP-enabled assistants, so researchers can query 10,000 years of social-complexity data in natural language. See packages/mcp-server/README.md.
  • 🌍 Echoes of History web app — a Next.js app for picking a civilisation, injecting a hypothetical change, and watching a forward-projected complexity trajectory with confidence bands.

Both are powered by the same estimation engine, which lives in the shared package.

About the Seshat Global History Databank

This project is built on top of Seshat — it does not generate historical data, it makes Seshat's data queryable and projectable.

Seshat (named after the ancient Egyptian goddess of wisdom and record-keeping) is a research databank founded in 2011 that systematically documents the social and political organisation of human societies across history. It calls itself "the most current and comprehensive body of knowledge about human history," structured so that the past can be studied with established scientific techniques. The databank is maintained by an international team of historians, social scientists, and data scientists, and underpins peer-reviewed research into the long-run evolution of social complexity.

Its scope is what makes counterfactual projection possible:

  • 864 polities (political societies) across 47 regions grouped into 10 macro-regions spanning Africa, Europe, Asia, the Americas, and Oceania
  • Coverage from roughly 3550 BCE to the modern era
  • 77 social-complexity variables (territory, population, settlement hierarchies, infrastructure, information systems, money) — the basis for the PC1 composite complexity score
  • 49 warfare variables (military technology, fortifications, conflict patterns)
  • 26 general variables (capitals, languages, religions, degree of centralisation)

Learn more or explore the source data at seshat-db.com. See Data provenance below for the specific datasets and licensing this project relies on.

Repository structure

This is a pnpm workspace monorepo:

Package What it is
packages/shared TypeScript types, the forward-projection model, scenario catalogue, and BCE/CE year utilities. Built first — the other packages depend on it.
packages/mcp-server MCP server exposing 9 Seshat tools. Independently publishable for researchers.
packages/web The "Echoes of History" Next.js 16 web app.
data/etl Python ETL pipeline that ingests Seshat data into Supabase (separate venv).
data/schemas Supabase SQL migrations, indexes, and RPC functions.

Quickstart

git clone https://github.com/mosse/seshat-mcp
cd seshat-mcp
pnpm install

# The shared package MUST be built before anything else
pnpm --filter @seshat/shared build

You'll need a Supabase project loaded with Seshat data (run the migrations in data/schemas and the ETL in data/etl). Copy the .env.example files in packages/mcp-server/ and packages/web/ and fill in:

Variable Used by Notes
SUPABASE_URL both Supabase project URL
SUPABASE_SERVICE_KEY both Service-role key — server-side only
ANTHROPIC_API_KEY web Narrative generation

Run the MCP server

pnpm --filter @seshat/shared build
pnpm --filter @seshat/mcp-server build
pnpm --filter @seshat/mcp-server dev

Then point Claude Desktop at it — see the MCP server README for config.

Run the web app

pnpm --filter @seshat/shared build
pnpm --filter web dev

The 9 MCP tools

Tool Purpose
search_polities Find historical polities by name, region, or active year
get_polity_detail Complexity scores and key variables for one polity
get_variables Variable values for a polity over time
get_complexity_timeline PC1 composite complexity trajectory across centuries
compare_polities Side-by-side comparison of 2–5 polities
find_analogous_polities Similar societies via cosine similarity on feature vectors
get_technology_diffusion How a technology spread across regions over time
run_counterfactual_estimate Project complexity under hypothetical changes with Monte Carlo confidence bands
get_region_snapshot All polities active in a region during a given century

Full parameter tables are in the MCP server README.

Testing

# MCP server (vitest) — 56 tests
pnpm --filter @seshat/mcp-server test

# Web app end-to-end (Playwright) — 34 tests, Desktop Chrome + mobile
pnpm --filter web exec playwright test

# ETL (pytest) — 75 tests, requires venv
cd data/etl && source .venv/bin/activate && pytest tests/

How estimates work — and their limits

The counterfactual engine uses an illustrative approximation of the dynamic regression model from Turchin et al. (2022), Disentangling the evolutionary drivers of social complexity (Science Advances), with Monte Carlo sampling over the model's residual noise to produce confidence bands.

⚠️ The current coefficients are approximate placeholders, not the published fit. Sourcing/fitting the real model (and other honesty fixes) is tracked in IMPROVEMENT_PLAN.md. Until then, read projections as directional illustrations, not authoritative estimates.

A few things to keep in mind when reading any projection:

  • Uncertainty is a feature. Every projection ships with confidence bands and an explicit confidence_limits section. The bands capture statistical noise within the model — not the full range of historical contingency. Treat the central line as one illustrative path, not a prediction.
  • Eurasian validation. The regression model was validated on Eurasian data. Projections for the Americas and Oceania-Pacific carry a prominent caveat and should be read with extra caution.
  • Year convention. BCE years are negative integers (year 0 = 1 BCE, astronomical). They're displayed as "500 BCE", never "-500".

Data provenance

  • Equinox-2020 — Seshat Global History Databank (Turchin et al., 2015). CC BY-NC-SA 4.0. Zenodo
  • Cliopatria — Seshat geospatial boundaries. CC BY 4.0. GitHub
  • Counterfactual model — Turchin et al. (2022). Disentangling the evolutionary drivers of social complexity. Science Advances.

License

Code is MIT. The underlying Seshat data is CC BY-NC-SA 4.0 — any use of the data must be non-commercial and attributed.

Recommended Servers

playwright-mcp

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.

Official
Featured
TypeScript
Magic Component Platform (MCP)

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.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

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.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

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.

Official
Featured
TypeScript
Kagi MCP Server

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.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

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.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured