Menstrual Cycle Prediction

What it grounds

Give enterprise health agents cycle prediction through the same data layer, computing period, ovulation and fertile windows on demand.

Ways to ground with it

Ground answers in real data

Enable Menstrual Cycle Prediction and it becomes a tool your agent calls over MCP to check itself against live data before it answers — grounding the response instead of guessing.

On the same endpoint and key

Menstrual Cycle Prediction rides the one VerveContext MCP endpoint alongside 300+ other sources, so adding it to an agent is a toggle, not an integration.

Traceable and current

The agent pulls menstrual cycle prediction at answer time and can carry the source with the result, so the reasoning stays current and auditable.

Connect over MCP

Point your agent framework at the VerveContext endpoint. Every enabled source — this one included — becomes a tool the model can call. No per-source plumbing.

context.json

{
  "mcpServers": {
    "vervecontext": {
      "url": "https://api.vervecontext.com/v1/mcp",
      "headers": { "x-api-key": "vc_live_••••" }
    }
  }
}

# “menstrualcycle” is now a tool your agent can call

What it returns

Each call returns structured fields your agent can read.

last_period_date
cycle_length
period_length
cycles_calculated
cycles
current_status
averages
disclaimer

Menstrual Cycle Prediction, answered

How to ground an agent in it over MCP.

How do I give my agent Menstrual Cycle Prediction?

Enable it on VerveContext and point your agent framework at the VerveContext MCP endpoint. Menstrual Cycle Prediction then appears to the model as a tool named menstrualcycle, which it calls when a task needs it — no per-source plumbing.

Which agents and frameworks can use it?

Any MCP client — Claude, Cursor, LangChain, and custom agents all speak the Model Context Protocol. Menstrual Cycle Prediction is exposed as a standard tool, so nothing source-specific is required.

Can the agent cite Menstrual Cycle Prediction?

Menstrual Cycle Prediction is a capability rather than a factual source, so it isn't something to "cite" — but it runs on the same real-data layer, over the same MCP endpoint and key as every grounding source.

How many credits does a Menstrual Cycle Prediction call cost?

Each call costs 1 credit. Scoped keys let you grant an agent only the sources it should reach, and usage logs show exactly what was pulled to ground each answer.

How current is the data?

Menstrual Cycle Prediction is served live from APIVerve's production data engine and pulled at the moment the agent calls it, so answers reflect the world now rather than the model's training cutoff.

Where does the data come from, and what shows on my bill?

VerveContext runs on APIVerve, our production data engine; Menstrual Cycle Prediction is one of 300+ sources on the same key. Invoices and card statements show APIVERVE.

Ground answers in real data

On the same endpoint and key

Traceable and current

Menstrual Cycle Prediction, answered

Ground your agents in Menstrual Cycle Prediction. Connect over MCP, enable the source, and every answer carries its receipts.

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