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Layerz MCP

Financial models your AI agent builds and edits: items, formulas, dependencies, branches. Exports to Excel.

Layerz is a structured spreadsheet for financial models, ready for AI. This is its remote MCP server. Claude, ChatGPT, Codex or any MCP client works on the model structure itself, not on loose cells, so formulas hold when the agent changes an assumption and the model is still there next session.

Layerz MCP connector

The server is hosted. There is nothing to install or run locally: this repository holds the setup instructions and the metadata MCP directories read.


Why

Ask an AI to build a financial model and you get a workbook in 30 seconds. Then one assumption changes: links break, values get hardcoded, and the next session starts from scratch.

Layerz keeps the structure of a model (items, formulas with named references, the dependency graph, timelines) apart from its data (values, scenarios, actuals). The agent edits that structure through MCP tools, every change is versioned with a readable summary, and Excel is an export format: clean, standard .xlsx with live formulas, the file you send to people who never see the agent.

Related MCP server: mendix-mcp-server

Setup

One click

Client

Link

Claude (claude.ai, desktop app, Cowork)

layerz.cc/claude opens Settings → Connectors with Layerz pre-filled

ChatGPT and Codex

layerz.cc/chatgpt opens the Layerz plugin in the ChatGPT directory

Confirm, sign in to Layerz, done.

Cursor

Add Layerz to Cursor

Or add it to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "layerz": { "url": "https://layerz.cc/mcp" }
  }
}

Cursor opens the Layerz sign-in the first time a tool is called.

By hand

Claude Code

claude mcp add -t http layerz https://layerz.cc/mcp

Then run /mcp to authenticate in the browser.

OpenAI Codex

codex mcp add layerz --url https://layerz.cc/mcp
codex mcp login layerz

Claude, without the link: Settings → Connectors → Add custom connector → https://layerz.cc/mcp

ChatGPT, without the directory: Settings → Security and login → Developer mode, then Plugins → + → https://layerz.cc/mcp

Mistral Le Chat: Settings → Connectors → Add custom MCP connector → https://layerz.cc/mcp

OpenCode: add to opencode.json, then run opencode mcp auth layerz

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "layerz": {
      "type": "remote",
      "url": "https://layerz.cc/mcp",
      "oauth": {}
    }
  }
}

Grok: grok.com/connectors → New Connector → Custom → https://layerz.cc/mcp

Antigravity CLI (formerly Gemini CLI): add to mcp_config.json

{
  "mcpServers": {
    "layerz": { "serverUrl": "https://layerz.cc/mcp" }
  }
}

Any other MCP client: point it at https://layerz.cc/mcp over streamable HTTP. Clients without OAuth can send a Layerz API key, created in the Layerz app, as a bearer token.

What the agent can do

29 tools, grouped by job. Read-only tools are annotated as such, so clients can run them without asking.

Job

Tools

Find and read models

layerz_list_models, layerz_get_model, layerz_read, layerz_dependencies, layerz_validate_model

Build and edit

layerz_create_model, layerz_build_from_blueprint, layerz_list_templates, layerz_patch, layerz_match_items

Conventions

layerz_get_conventions, layerz_set_finance_md, layerz_set_custom_instructions

Scenarios and actuals

layerz_update_branch, layerz_delete_branch, layerz_import_branch, layerz_list_transactions

Import files and sources

layerz_create_upload_url, layerz_parse_file, layerz_list_integrations, layerz_manage_integration, layerz_list_mappings, layerz_manage_mapping, layerz_external_ref

Versions

layerz_history, layerz_diff, layerz_manage_version

Share and export

layerz_share_model, layerz_export

The server also exposes reference resources the agent reads on its own: modeling-guide://reference (session workflow, modeling patterns, validated patch examples), formula-spec://reference (the formula language) and format-spec://reference (number formats).

Using it well

The guidance lives on the server, so it is never out of date on your machine:

  • Conventions travel with the model. Each model carries a FINANCE.md (currency, units, sign convention, how the statements are built), and each workspace carries standing instructions for agents. The agent receives both when it opens a model.

  • The modeling guide comes from layerz_get_conventions { include_guide: true }. Agents read it at the start of a session.

  • Recipes (prompts for budgets, three-statement models, monthly closes, LBOs) are in the cookbook.

A good first prompt:

Build a three-statement model for a B2B SaaS company: 2 M€ ARR, 40 % growth, 18-month runway. Ask me what you need to know first.

Pricing

Free for one person working alone: unlimited models, MCP and REST API, Excel and JSON export. A paid plan starts when a second person edits the same models. Details at layerz.cc/pricing.

  • finance-md: the open standard for financial conventions an agent reads

  • cookbook: recipes for financial work with AI agents

  • layerz.cc: the web app, where people review, share and collaborate on the same models

Versioning

One version, the server's. The number published to the official MCP Registry (io.github.layerzlabs/layerz, from the server.json of the Layerz code repo) is the only version; lhm.plugin.json and .cursor-plugin/plugin.json here mirror it, never one of their own. A change to this repository alone (a tool declared, a description reworded) ships under the current version. node scripts/check-version.mjs (run on every push and weekly) fails when the manifests disagree or run behind the registry, and only warns when a publish is pending.

Support

Questions or stuck on setup: contact@layerz.cc. Bugs and requests: open an issue in this repository.

License

The contents of this repository (documentation and metadata) are released under the MIT License. The Layerz server and web app are proprietary.

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