Skip to main content
Glama

text-to-image

The one-wire hello-world of noodling — describe a scene, get an image. text -> image; returns image (nano-banana-2-lite) saved to disk (file path in result). Runs on NanoGPT — $0.06 deposit per call, paid in Nano (XNO) — settles at actual model cost + 20%, change returned; no account needed; last run $0.04, ~10s.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
TextNodefault: "a red panda astronaut drifting over neon-lit Tokyo at night, cinematic, 35mm"; optional
_payment_idNoPayment id from this tool's previous payment-required response. Phase 3 only: after /x402/watch closes with status paid, call again with the same arguments plus this id to open the RESULTS stream. Do not pass it while payment is still pending — monitor the watch SSE first.

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of disclosing behavioral traits. It covers payment behavior ($0.06 deposit, cost+20% settlement, Nano), latency (~10s), and the side effect of saving an image to disk. It doesn't explain failure modes or the two-phase payment flow in detail, but it provides meaningful context beyond a bare 'text to image' statement.

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?

The description is brief, using two sentences that front-load the purpose before adding cost and output details. However, the opening phrase 'one-wire hello-world of noodling' is vague and consumes space that could clarify the tool's role. Overall it's compact and efficient, but the initial flavor text is not maximally informative.

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

Completeness3/5

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

The tool involves payment and a potential two-phase flow (deposit, payment watch, then _payment_id to retrieve results), but the description only mentions cost and latency, omitting how the payment workflow integrates with the _payment_id parameter. The schema covers _payment_id, but the description doesn't connect that context, leaving gaps in understanding the full usage sequence.

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%, so the baseline is 3. The description adds minimal parameter insight beyond 'text -> image' and does not elaborate on the _payment_id parameter or its phased usage, which remains only in the schema. It adds a little context but doesn't significantly enhance understanding of the parameters.

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

Purpose5/5

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

The description clearly states the tool converts text to an image ('text -> image') and that the result is saved to disk. This specific verb+resource distinguishes it from sibling tools like edit-a-photo, combine-images, or favicon, which operate on existing images rather than generating new ones from text.

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

Usage Guidelines3/5

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

The description implies usage ('describe a scene, get an image') but provides no explicit guidance on when to choose this tool over alternatives, nor does it list exclusions or prerequisite conditions. There is no mention of when not to use it, so the guidance is only implicit.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.9/5.0
Disambiguation4/5

Most tools target distinct media transformations (text-to-image, text-to-video, text-to-audio), and the overlapping ones (combine-images vs edit-a-photo, text-to-image vs favicon vs render-a-mockup) have clear specialization in their descriptions. An agent can generally tell them apart, though a couple of image-editing and image-generation tools could be confused.

Naming Consistency3/5

Names use hyphens consistently but follow mixed conventions: verb_noun (combine-images, edit-a-photo, render-a-mockup), noun_to_noun (photo-to-video, text-to-image), single words (deslop, favicon, sing), and compound nouns (image-model-arena, talking-avatar). The variety is readable but lacks a uniform pattern.

Tool Count5/5

Ten tools is an ideal size for a creative media server, covering image, video, audio, and text generation without feeling bloated. Each tool earns its place by addressing a distinct type of creative task.

Completeness4/5

The server covers core creative generation workflows across image, video, audio, and text, with both generation and editing capabilities. Minor gaps exist, such as no direct text-to-video without an intermediate image and no generic audio effects, but the major modalities are represented.