Vector normalize

vector-normalize · version 1.0.0 · Vectors & matrices · free, no key needed

Scale a finite-number vector to unit Euclidean length.

Use when you need to: vector normalization · unit vector · make vector unit length.

Supported

• vector normalization
• unit vector
• make vector unit length

Not supported

• normalizing the zero vector (undefined direction)
• normalization by a norm other than L2

Behavior

• Input vector is an array of 1 to 64 finite JSON numbers, each bounded to at most 1000000 in magnitude.
• The Euclidean (L2) norm is computed with IEEE-754 double-precision Math.sqrt over the sum of squares.
• Optional input tolerance (default 1e-9, range 0 to 1) is the minimum norm accepted; vectors whose norm is at or below tolerance are rejected as having no well-defined direction.
• The result is result[i] = vector[i] / norm, computed in IEEE-754 double precision; -0 is normalized to 0.
• Every result entry is checked to be a finite number before it is returned; a non-finite intermediate is rejected as invalid input rather than silently serialized.

Input

• vector (array of number, required): min items 1; max items 64
• tolerance (number, optional): min 0; max 1

Output

• result (array of number, required)

Limits

• max dim: 64
• max abs value: 1000000

Example

Request input:

{
  "vector": [
    3,
    4
  ]
}

Response:

{
  "result": {
    "result": [
      0.6,
      0.8
    ]
  }
}

How to call it

MCP

Connect https://computefirst.net/mcp (setup), then call execute with:

{
  "id": "vector-normalize",
  "version": "1.0.0",
  "input": {
    "vector": [
      3,
      4
    ]
  }
}

HTTP (no key)

curl -X POST https://computefirst.net/v1/tools/vector-normalize/versions/1.0.0/execute \
  -H "Content-Type: application/json" \
  -d '{"vector":[3,4]}'

The machine-readable contract is at /v1/tools/vector-normalize/versions/1.0.0.

CLI

node cli.mjs run vector-normalize 1.0.0 --input input.json --base-url https://computefirst.net

Get the client at /clients/cli/.

Related tools

• Vector norm: Compute the L1, L2 (Euclidean), or L-infinity (Chebyshev) norm of a finite-number vector.
• Matrix vector multiply: Multiply an MxN finite-number matrix by a length-N finite-number vector, producing a length-M vector.
• Vector add: Add two equal-length finite-number vectors elementwise.
• Vector angle: Compute the angle in radians between two equal-length finite-number vectors.
• Vector approx equal: Compare two equal-length finite-number vectors for approximate equality within an absolute tolerance.
• Vector cross: Compute the 3-dimensional cross product of two finite-number vectors.