Tanod

imagepeek: neural 2x upscale

upscale_image

Idempotent

imagepeek: Upscale a small image 2x with a neural model. Returns creates a 2x neural derivative of one PNG, JPEG or WebP using offline QuickSRNet Small (BSD-3-Clause). Neural x2 derivative, not recovered detail or OCR. PNG/JPEG/WebP <=250000 pixels, 2048 per side; output PNG <=1 MP. EXIF oriented, alpha white, frame 0. Failures not charged.. Up to 45 s. Price: USD 0.01. No free tier.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "upscale_image"
  }
}

Input Schema

Name	Required	Description	Default
`url`	No	Public http(s) URL of the image (at most 10 MB; private, internal and IP-literal hosts are refused); or use image_base64.
`output`	No	base64 (default): inline. url: stored for 24 hours; link, metadata.	base64
`image_base64`	No	The image as standard or url-safe base64 (at most 10 MB decoded; a data:image/...;base64, prefix is fine); or use url.

Output Schema

Name	Required	Description
`url`	No	Public link.
`bytes`	No	Bytes.
`error`	No	Only on an error result: an object {code, message}, or the reason string of an x402 PaymentRequired object.
`input`	No
`notes`	No
`scale`	No
`width`	No
`format`	No
`height`	No
`sha256`	No	SHA-256.
`source`	No
`final_url`	No
`operation`	No
`expires_at`	No	UTC expiry.
`source_url`	No
`data_base64`	No
`content_type`	No	MIME type.

Schema Changelog

Changes observed during successful MCP inspections.

AddedOct 29, 2026

TDQS

A4.1/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations only cover the safety profile (readOnlyHint false, idempotent true, destructive false, openWorld true); the description adds what they cannot: USD 0.01 price, no free tier, failures not charged, up to 45 s latency, EXIF auto-orientation, alpha flattened to white, and frame 0 only. Those are exactly the operational facts an agent needs before paying for a call.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the core action and tightly packed; every clause (limits, model, cost, timing, failure billing) carries information. It is slightly marred by telegraphic fragments like "EXIF oriented, alpha white, frame 0" and the garbled "Returns creates a 2x neural derivative," which cost a bit of readability.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return-shape explanation is correctly omitted. Between the description and schema, an agent knows input formats, size ceilings, output format options, cost, latency, and failure billing — nothing material is missing for a correct, well-scoped call.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, including the url-vs-image_base64 either/or and the output enum semantics, so the description need not re-explain inputs. It only mentions input format constraints obliquely ("one PNG, JPEG or WebP"), adding nothing beyond the schema — baseline 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ("Upscale a small image 2x with a neural model") and pins down the method (offline QuickSRNet Small), which separates it from the plain-resize and OCR siblings. It stops short of naming image_resize as the non-neural alternative, so the differentiation is implied rather than explicit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives clear applicability conditions (small image, hard caps of 250000 pixels and 2048 per side) and an explicit negative case: "Neural x2 derivative, not recovered detail or OCR," which steers away from using this for detail recovery or text extraction. No sibling alternative is named for cases outside those bounds.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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