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propzapi

by io.github.paperandbeyond23-gif

generate_image

Render an on-brand image from a template and your data, and return the image. Use list_templates first to see template ids and their variables. 1 credit per image; cost returned in _meta.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formatNopng
verifyNoStrings that must render as visible text. The result reports whether each one landed (found and not clipped) — use it to confirm the exact values you passed made it into the image.
receiptNoReturn a render receipt: image_sha256, the text that actually rendered, per-string checks + misses, viewport, and timing. Lets you trust the render without looking at the pixels.
templateYesTemplate id from list_templates: a built-in (e.g. og-article) or one of your own tpl_... ids.
modificationsNovariable -> value overrides, e.g. {"title":"...","accent":"#b45cff"}.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / properties / receipt
      Added value: +{
+  "description": "Return a render receipt: image_sha256, the text that actually rendered, per-string checks + misses, viewport, and timing. Lets you trust the render without looking at the pixels.",
+  "type": "boolean"
+}
  2. Changed1 schema field changed
    • addedInput schema / properties / verify
      Added value: +{
+  "description": "Strings that must render as visible text. The result reports whether each one landed (found and not clipped) — use it to confirm the exact values you passed made it into the image.",
+  "items": {
+    "type": "string"
+  },
+  "type": "array"
+}
  3. Added

TDQS

A4.4/5.0
Behavior4/5

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

No annotations, so the description carries the full burden. It discloses a cost side-effect ('1 credit per image; cost returned in _meta') and the prerequisite of listing templates. It clearly identifies the operation as template-based rendering, not generative AI. It doesn't specify output format or error behavior, but this is solid for a non-destructive render tool.

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

Conciseness5/5

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

Two concise, front-loaded sentences. The first sentence states the purpose; the second gives prerequisite and cost. No wasted words or redundancy.

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

Completeness4/5

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

For a tool with a rich 5-param schema and no output schema, the description covers the core return behavior (image), prerequisite (list_templates), and cost. It doesn't explicitly state how the image is delivered (bytes/URL) or failure modes, but the schema's verify/receipt parameters hint at these. Overall adequate for an agent to select and invoke correctly.

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

Parameters4/5

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

Schema coverage is high (80%) with descriptions for verify, receipt, and template. The description adds workflow context by stating template ids and variables come from list_templates, which clarifies both the template and modifications parameters. This goes beyond the schema and earns a 4.

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 'Render an on-brand image from a template and your data, and return the image' with a specific verb and resource. It distinguishes itself from siblings list_templates (listing template ids) and screenshot_url (capturing a URL) by emphasizing template-based rendering.

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?

Explicitly instructs to 'Use list_templates first to see template ids and their variables', establishing a clear prerequisite workflow. It also mentions credit cost and _meta return, giving practical usage context. No explicit alternatives/negations, but the guidance is sufficient for when to use.

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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TDQS

A4.3/5.0
Disambiguation5/5

Each tool has a distinctly different purpose: generate_image creates images from templates, list_templates provides template metadata, and screenshot_url captures web pages. There is no overlap or ambiguity between these actions.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern: generate_image, list_templates, screenshot_url. The naming is uniform and predictable.

Tool Count5/5

With 3 tools, the server is well-scoped for its purpose of image generation and screenshots. Each tool is essential and there are no redundant or missing core functions.

Completeness5/5

The tool set covers the full workflow: discover templates, generate an image, and screenshot a URL. There are no obvious gaps; even cost information is provided via _meta.

Resources