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jakobautomation

agentsvc-mcp

chart_render

Convert JSON chart configs or type, labels, and datasets into PNG images with Chart.js for bar, line, pie, doughnut, scatter, radar, polarArea, and bubble charts.

Instructions

Chart Rendering (Chart.js) — Render a chart to PNG from JSON: bar, line, pie, doughnut, scatter, radar, polarArea or bubble. Pass a full Chart.js config (config) or the shortcut type + labels + datasets [{label, data}] + title. Rendered with Chart.js in headless Chromium, no external requests. Params: config or type/labels/datasets, title, width and height (100 to 2000 px, default 800x500), background (CSS color, default white). Returns image_base64 (PNG). Cost: $0.009 USDC per call. Latency p99: 6000ms.

Input Schema

NameRequiredDescriptionDefault
typeNo
titleNo
widthNo
configNoFull Chart.js configuration {type, data, options}
heightNo
labelsNo
datasetsNo
backgroundNowhite

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so well: it discloses the rendering environment (headless Chromium), a security-relevant guarantee (no external requests), the return payload (image_base64 PNG), per-call cost ($0.009 USDC) and p99 latency (6000ms). These are exactly the traits an agent cannot infer from the schema.

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, then formats, then input modes, then params, then cost/latency — a sensible ordering with little waste. It is dense and mostly one long paragraph, but every clause (cost, latency, no external requests) earns its place.

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?

For an 8-parameter tool with nested objects and no output schema, the description covers all inputs, the two usage modes, the return format, and the operational cost/latency profile. Nothing an agent needs to call it correctly is missing.

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 description coverage is only 13%, so the description must compensate, and it does: it explains the config-vs-shortcut relationship and the datasets [{label, data}] shape that the schema leaves opaque. It partly repeats the width/height ranges and defaults already visible in the schema, keeping it from a top score.

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?

States a precise verb+resource ('Render a chart to PNG from JSON') and enumerates the supported chart types, which immediately separates it from generic siblings like diagram_render or satellite_image. An agent knows exactly what it produces (a PNG chart) without opening the schema.

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 invocation context by spelling out the two modes: a full Chart.js 'config' or the shortcut 'type + labels + datasets + title'. It does not, however, state when NOT to use it or name an alternative sibling (e.g. diagram_render), so it stops short of explicit routing.

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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