MCP Server
Yeer's MCP server turns demand forecasting into a tool your AI agent can call. Connect Claude, Cursor, or your own agent to ask about your forecasts in plain language — or hand it a raw time series and get a forecast back, with no data onboarding at all.
Planners already know what they want to ask; the dashboard is just where the answer happens to live. With the MCP server the question goes wherever the planner already works — a chat window, an IDE, a scheduled agent — and the answer comes back with the same numbers the dashboard would show.
“What is our September forecast for plant HH50, and which SKUs drive it?”
“Which ten material-plant combinations have the worst accuracy right now?”
“Are we over- or under-forecasting by plant this quarter?”
“Why is the forecast for this SKU up in Q4?”
“How does the toothpaste forecast compare with the sales target?”
“Here is 24 months of history for a new listing — forecast the next six.”
On your own forecasts
Thirteen read tools cover the questions a planner actually asks: totals, accuracy, bias, drivers, targets, promotions, and an ad-hoc query over history. Every number comes from the best model per entity, selected by backtested accuracy, so an agent can never quote a weaker model than the team uses.
Requires a Yeer project with data in it. Keys are scoped to one organization.
On any time series
The forecast tool takes a series inline — ten points or more, daily through quarterly — picks a zero-shot model, and returns the forecast with prediction intervals. Turn backtesting on and it evaluates candidate models on a holdout and reports the accuracy it measured, so the agent knows how much to trust the number.
No project, no upload, no model choice. Useful for one-off questions and for agents that generate their own data.
Fourteen tools, all read-only over your data. There is deliberately no model-selection tool: choosing a model is Yeer's job, not the agent's.
Create a key in the developer console, drop the endpoint into your MCP client, and ask the agent to list your forecast projects. The server speaks Streamable HTTP and authenticates with a bearer token, so there is nothing to install locally.
https://yeer.ai/mcp
{
"mcpServers": {
"yeer-forecasting": {
"url": "https://yeer.ai/mcp",
"headers": { "Authorization": "Bearer yeer_sk_your_key_here" }
}
}
}curl -X POST https://yeer.ai/mcp \
-H "Authorization: Bearer yeer_sk_your_key_here" \
-H "Content-Type: application/json" \
-H "Accept: application/json, text/event-stream" \
-d '{"jsonrpc":"2.0","id":1,"method":"tools/list"}'Signed in already? Keys, live rates and a per-tool usage log live in the developer console.
The MCP server reads the same projects your team uses on the enterprise platform — the ones behind demand forecasting and demand sensing. Nothing to re-onboard: create a key and the forecasts your planners already trust are available to your agents.
It is a Model Context Protocol server that exposes Yeer's demand forecasting as tools an AI agent can call. Point Claude, Cursor, or any MCP client at https://yeer.ai/mcp with an API key, and the agent can read your forecasts, accuracy and drivers in plain language — or forecast a raw time series that has never been uploaded to Yeer.
Any client that speaks Streamable HTTP with a bearer token, including Claude Desktop, Claude Code, Cursor, and custom agents built on the MCP SDKs. The server is stateless, so it sits behind a normal load balancer and needs no session affinity.
Not for the forecast tool. It takes a time series inline — at least ten points, daily through quarterly — picks a model, and returns the forecast with prediction intervals, so any agent can forecast without onboarding data. The other thirteen tools read forecasts that already live in your Yeer projects, so those need an account with data in it.
You never pick one. Every tool returns Yeer's AI-selected best model per entity, chosen by backtested accuracy — exactly what the dashboard shows — so an agent cannot accidentally quote a weaker model than the planning team uses. The inline forecast tool selects a zero-shot model the same way, and with backtesting on it reports the accuracy it measured on a holdout.
No. Every tool that touches your data is read-only. There is no tool to train, edit, override, or delete, and each API key is scoped to one organization, so an agent can only ever see that organization's projects.
Usage-based credits, drawn per call: a read or insight call costs one credit, a natural-language query costs ten, and an inline forecast is priced from the size of the series and the horizon, with a surcharge when it runs a model bakeoff. Every new organization starts with free credits, and the developer console shows the live rates, your balance and a per-tool usage log.
Read and insight tools allow 120 calls per minute per organization; the compute-heavy inline forecast tool allows 30. Both are per organization rather than per key, so adding keys for different agents does not change the ceiling.
No. Customer data is never used to train our models, and the MCP server adds no exception: it reads forecasts and history that already exist in your tenant and returns them to your agent.
Talk to us about access, or create a key and run the first call today.
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