Skip to main content
Glama

generate_proposal_image

Create process diagrams or CONOPS visuals for government and defense proposals. Provide a caption and a proposal document to generate a refined PNG image.

Instructions

Generate a proposal diagram image using the Paper Banana pipeline.

Provide either source_file (recommended) or source_context — not both. To resume a previous run, set resume_from to the path of the run-state directory (e.g. output_run/) created during the original run.

Args: caption: The caption or communicative intent for the image (required). source_file: Path to the proposal document (markdown or text). The Extractor agent will read this file and pull the relevant section automatically. Recommended over source_context for full documents. section: Section hint for the Extractor — number, name, or both (e.g. '1.1', 'Development Services', '1.1 Development Services'). Only used when source_file is provided. source_context: Raw proposal section text. Use this only when passing a pre-extracted section directly (not a full document). image_type: Diagram type — 'process', 'conops', or 'auto' (auto-detected). iterations: Number of Visualizer ↔ Critic refinement rounds (1-5). Default 3. output_path: File path where the generated PNG will be saved. Default 'output.png'. save_artifacts: If True, save per-iteration PNG and critique JSON to {output_stem}_artifacts/ alongside the final image. Default True. export_schema: If True (and save_artifacts is also True), extract a structured diagram schema (elements, connectors, layout) from each iteration's image and merge it into the critique JSON as 'diagram_schema'. Default True. brand: Named brand profile to apply — 'mlst' (purple/blue) or 'default' (government/defense palette). Default 'mlst'. resume_from: Path to a run-state directory ({output_stem}_run/) created by a previous run. When set, the extractor/optimizer/classifier/ retriever/planner/stylist stages are skipped and the pipeline jumps directly to the Visualizer ↔ Critic refinement loop using the saved description, caption, and image type. Leave empty for a fresh run. user_feedback: Free-text feedback about the previous run's output. Injected as a prefix into the critic's system prompt so all refinement iterations take the feedback into account. Only meaningful when resume_from is also set (but can be used on fresh runs too). aspect_ratio: Desired output aspect ratio — '16:9', '4:3', '1:1', or '3:4'. Leave empty (default) to let the Planner recommend a ratio based on the diagram content. When provided, this hard-overrides the Planner's recommendation.

Returns: Absolute path to the saved PNG image.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
brandNomlst
captionYes
sectionNo
image_typeNoauto
iterationsNo
output_pathNooutput.png
resume_fromNo
source_fileNo
aspect_ratioNo
export_schemaNo
user_feedbackNo
save_artifactsNo
source_contextNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

No annotations are provided, so the description carries full burden. It comprehensively discloses pipeline stages (Extractor, Visualizer↔Critic refinement), default iterations, artifact saving, schema export, and resume behavior. No contradictions.

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 well-structured with an Args section and a Returns line. While somewhat lengthy, every sentence adds value. It is front-loaded with the main purpose and key usage note.

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?

Given 13 parameters, no annotations, and presence of an output schema (which description supplements with return path), the description is complete. It covers all inputs, behavior, and output format.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must compensate. It thoroughly explains each of the 13 parameters, including defaults, constraints (mutual exclusivity of source_file/source_context), and purpose. This adds significant value beyond the bare schema.

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 states that the tool generates a proposal diagram image using the Paper Banana pipeline. It clearly distinguishes from siblings (add_example, get_version) by specifying the unique function and pipeline details.

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?

The description provides explicit guidance: 'Provide either source_file (recommended) or source_context — not both.' It also explains resume behavior and when to use user_feedback. However, it does not explicitly state when not to use the tool or list alternatives beyond siblings.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/lexicalninja/paper-banana'

If you have feedback or need assistance with the MCP directory API, please join our Discord server