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Pinpole MCP Server

Build architecture (design + draw on canvas)

pinpole_build_architecture

Turns natural-language system descriptions into validated cloud architecture diagrams on a Pinpole canvas. Optionally runs cost simulations.

Instructions

Turn a natural-language system description into a validated cloud architecture and draw it on the user's Pinpole canvas. Returns a canvas URL the user can open to see and edit the live ReactFlow diagram (a draw.io replacement). Optionally runs a cost simulation. Use this when the user wants to design or visualize a system/architecture.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesWhat to build, e.g. 'a serverless image upload pipeline with thumbnailing'
titleNoTitle for the new project/workspace
projectIdNoDraw into an existing project instead of creating one
workspaceIdNoDraw into an existing workspace instead of creating one
simulateNoIf provided, also run a cost simulation
Behavior3/5

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

With no annotations, the description fully bears the transparency burden. It states that it returns a canvas URL and optionally runs a cost simulation. However, it does not disclose details about validation, potential destructive actions, auth requirements, or rate limits. This is adequate but not thorough.

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?

Three sentences: the first states the core action, the second adds return value, the third adds optional cost simulation and usage guidance. No wasted words, and the most important information is front-loaded.

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?

Given the moderate complexity (5 parameters, nested objects, 1 required) and no output schema, the description is mostly complete. It explains the return (canvas URL) and optional cost simulation. It could mention that the prompt must be a system description, but schema already covers that. Slight gap in explaining validation.

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 coverage is 100% and all parameters have descriptions. The description adds 'Optionally runs a cost simulation' which reinforces the simulate parameter but doesn't add new meaning beyond the schema. Baseline 3 is appropriate.

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 it turns a natural-language system description into a validated cloud architecture and draws it on the canvas, specifying both the verb (build/draw) and the resource (architecture on canvas). It distinguishes from siblings like pinpole_create_architecture which may not draw, and pinpole_draw_on_canvas which may not create.

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 says 'Use this when the user wants to design or visualize a system/architecture.' This provides clear context. While it does not explicitly exclude other tools or list alternatives, the sibling context implies differentiation, and the guidance is direct.

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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