Fibonacci Generation for AI Agents

Overview

The Fibonacci Generator has two modes that share one response shape. With `count` (1 to 1000) it returns that many terms. With `maxvalue` it returns every term that does not exceed the ceiling, with a hard stop at 5,000 terms. `startfrom` (0 to 10,000) advances the generator before output begins. At the default of 0 the sequence opens with 0, 1, 1, 2 and the leading 0 counts toward `count`; at any higher value the first term returned is F(startfrom + 1), so `startfrom=10` begins at 89.

Terms and the running `sum` are accumulated as exact big integers and converted to JSON numbers only when the response is built. A JSON number holds integers exactly only up to 2^53, so from F(79) onward the returned digits are the nearest representable value rather than the exact term, and F(1476) is the largest term a number can hold, so the sequence stops there (a startfrom past 1475 returns an empty sequence); `sum` can still be null when the total overflows. For exact large values, use the sequence for its shape and compute the digits client-side.

`ratios` divides each term by the one before it, skipping any division by zero, rounds to six decimal places and keeps only the last five. `golden_ratio_approximation` is the final ratio, or null when the sequence is too short to form one, for example a single term. `first_value`, `last_value` and `count` describe the returned array, and `start_from` echoes the offset used.

Live Test Fibonacci Generation for AI Agents Source →

The tool

Once your client is connected to the VerveContext server, this appears in its tool list as FibonacciGenerationforAIAgents. 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": "FibonacciGenerationforAIAgents",
  "arguments": {
    "count": "10"
  }
}

You do not name the tool yourself; the model picks it. Asking about 10 in the terms this source covers is enough for it to reach for FibonacciGenerationforAIAgents 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": {
    "sequence": [
      0,
      1,
      1,
      2,
      3,
      5,
      8,
      13,
      21,
      34
    ],
    "count": 10,
    "start_from": 0,
    "first_value": 0,
    "last_value": 34,
    "sum": 88,
    "ratios": [
      1.666667,
      1.6,
      1.625,
      1.615385,
      1.619048
    ],
    "golden_ratio_approximation": 1.619048
  }
}

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 start_from: 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

Math Education

Teach Fibonacci sequences and golden ratio concepts in educational applications and tools

Algorithm Testing

Generate test data for algorithms and performance testing using Fibonacci sequences

Visual Design

Apply Fibonacci ratios and golden ratio proportions in design and layout calculations

Data Analysis

Use Fibonacci numbers in technical analysis, trading algorithms, and pattern recognition

Other ways to use Fibonacci Generation for AI Agents

Set up Fibonacci Generation 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.

Related

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