Layerz MCP
OfficialThis server has a remotely hosted endpoint. Open its connector listing for connection details, authentication requirements, and setup instructions:
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.
Endpoint:
https://layerz.cc/mcp(streamable HTTP)Auth: OAuth 2.1 with PKCE and dynamic client registration, or a Layerz API key
Docs: layerz.cc/for-agents · llms.txt
Registry:
io.github.layerzlabs/layerzon the official MCP Registry
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
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/mcpThen run /mcp to authenticate in the browser.
OpenAI Codex
codex mcp add layerz --url https://layerz.cc/mcp
codex mcp login layerzClaude, 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 |
|
Build and edit |
|
Conventions |
|
Scenarios and actuals |
|
Import files and sources |
|
Versions |
|
Share and 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.
Related
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.
This server cannot be deployed
Maintenance
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