Ovulation Calculation for AI Agents

Overview

The calculation is pure date arithmetic with no medical model behind it. It assumes a fixed 14-day luteal phase, so ovulation is placed `cycle_length` minus 14 days after `last_period`: day 14 for the default 28-day cycle, day 7 for a 21-day cycle and day 21 for a 35-day cycle, the accepted range. Irregular cycles are not modelled, and the response includes a `disclaimer` field saying the figures are estimates.

`last_period` is a date in YYYY-MM-DD format, read as a calendar date with no time-zone shift, so the dates returned are the same calendar days you would count by hand. An unparseable date returns a 400.

`fertile_window` runs from five days before the estimated ovulation date through that date, six days in all. `most_fertile_days` is the last three of those days. `fertile_days` lists the six days individually, each with `day_relative_to_ovulation` from -5 to 0 and a `fertility_level` of medium for the first three and high for the last three. `next_period.date` is `last_period` plus `cycle_length`. `cycle_phases` reports the follicular phase as `cycle_length` minus 14 days, ovulation as one day and the luteal phase as 14.

`current_status` compares those dates with the server's current date: the phase is Follicular Phase before the window, Fertile Window inside it, Luteal Phase until the next period, and Menstruation once that date has passed, however long ago. The day counts go negative when a date is in the past. On the Free plan only `ovulation.date`, the echoed inputs and `disclaimer` are returned; all other fields are null.

Live Test Ovulation Calculation for AI Agents Source →

The tool

Once your client is connected to the VerveContext server, this appears in its tool list as OvulationCalculationforAIAgents. It is read-only and open-world — it fetches and never mutates anything on your side — so most clients call it without asking you to confirm.

{
  "name": "OvulationCalculationforAIAgents",
  "arguments": {
    "last_period": "2024-01-01"
  }
}

You do not name the tool yourself; the model picks it. Asking about 2024-01-01 in the terms this source covers is enough for it to reach for OvulationCalculationforAIAgents on its own — naming it explicitly also works, and is the way to force the call.

Connecting

One server URL covers every source in the catalog, including this one. Authorization is OAuth: the client opens a browser once, and there is no key to paste into a config file.

{
  "mcpServers": {
    "vervecontext": {
      "url": "https://api.vervecontext.com/v1/mcp"
    }
  }
}

https://api.vervecontext.com/v1/mcp

Per-client setup — Claude, Cursor, VS Code, ChatGPT — is on the MCP setup page.

Arguments

These are the properties on the tool's inputSchema, so a well-behaved client validates them before the call is made. Premium arguments are accepted on every plan but only take effect on plans that include them.

What the model gets back

The result carries a structuredContent object matching the tool's declared outputSchema, so a client reads fields without parsing prose. status is "ok" and error is null on success; a null field means the value was not available for that input, not that the call failed.

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{
  "status": "ok",
  "error": null,
  "data": {
    "last_period": "2024-01-01",
    "cycle_length": 28,
    "ovulation": {
      "date": "2024-01-15",
      "days_from_last_period": 14
    },
    "fertile_window": {
      "start": "2024-01-10",
      "end": "2024-01-15",
      "duration_days": 6
    },
    "most_fertile_days": {
      "start": "2024-01-13",
      "end": "2024-01-15",
      "duration_days": 3
    },
    "fertile_days": [
      {
        "date": "2024-01-10",
        "day_relative_to_ovulation": -5,
        "fertility_level": "medium",
        "description": "Fertile"
      },
      {
        "date": "2024-01-11",
        "day_relative_to_ovulation": -4,
        "fertility_level": "medium",
        "description": "Fertile"
      },
      {
        "date": "2024-01-12",
        "day_relative_to_ovulation": -3,
        "fertility_level": "medium",
        "description": "Fertile"
      },
      {
        "date": "2024-01-13",
        "day_relative_to_ovulation": -2,
        "fertility_level": "high",
        "description": "Most fertile"
      },
      {
        "date": "2024-01-14",
        "day_relative_to_ovulation": -1,
        "fertility_level": "high",
        "description": "Most fertile"
      },
      {
        "date": "2024-01-15",
        "day_relative_to_ovulation": 0,
        "fertility_level": "high",
        "description": "Most fertile"
      }
    ],
    "next_period": {
      "date": "2024-01-29",
      "days_from_last_period": 28
    },
    "cycle_phases": {
      "follicular_phase": {
        "duration_days": 14,
        "description": "From first day of period to ovulation"
      },
      "ovulation": {
        "duration_days": 1,
        "description": "Egg is released from ovary"
      },
      "luteal_phase": {
        "duration_days": 14,
        "description": "From ovulation to next period"
      }
    },
    "current_status": {
      "current_phase": "Menstruation",
      "is_fertile": false,
      "days_until_ovulation": -701,
      "days_until_next_period": -687
    },
    "disclaimer": "This calculator provides estimates only. Actual ovulation may vary. Consult a healthcare provider for medical advice."
  }
}

Response fields

Paths are relative to data. Premium fields are absent rather than zeroed on plans that do not include them, so check for presence instead of comparing to 0.

Why ground on it

A model can produce something that looks like this answer from its training data, and be confidently out of date or simply wrong. This source returns the current value in a shape you can check, which is the difference between an answer you can cite and one you have to hedge.

Point an evaluation at disclaimer: it is the field most worth pinning a claim to, and it is either present and current or absent — never plausibly invented.

Failure modes

Errors come back as tool errors carrying a sentence the model can act on, not a bare status code. Error handling covers the full list.

A call costs 1 credit each time the tool actually runs; a model that reasons about the tool without calling it costs nothing.

Use cases

Fertility Tracking

Build fertility tracking applications with accurate ovulation predictions based on cycle length and last period date

Conception Planning

Calculate conception probability windows and most fertile days to help users plan or prevent pregnancy

Menstrual Health

Create menstrual health apps with ovulation tracking, fertile window identification, and cycle insights

Family Planning

Provide family planning tools with science-based ovulation calculations and fertility awareness features

Other ways to use Ovulation Calculation for AI Agents

Set up Ovulation Calculation for AI Agents on VerveContext, or reach the same source a different way. Your VerveContext account and credits work on all of them — one key, one balance.

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