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ChartAnt

by net.chartant
OverviewDiscussionsAdmin

Calendar heatmap

generate_calendar_heatmap
Read-onlyIdempotent

Draw a calendar heatmap (GitHub-style contribution grid): one cell per day coloured by value. Use for daily activity over weeks or months. Returns a hosted PNG link, a share page, alt text and editable PowerPoint/Excel links.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "data": "<data>"
    },
    "name": "generate_calendar_heatmap"
  }
}

Input Schema

NameRequiredDescriptionDefault
dataYesOne row per day: {date: 'YYYY-MM-DD', value}.
themeNodefault (light) or dark background.
titleNoTitle shown above the chart.
widthNoImage width in pixels (default 720).
heightNoImage height in pixels (default 480).
brandThemeNoName of a brand theme saved on the user's account (their colours and footer), only when the user asks for their theme. Or "colorblind" for colours that people with colour-vision deficiency can tell apart (anyone can use it).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already establish read-only, idempotent, closed-world, non-destructive behavior, and the description goes further by enumerating the deliverables: hosted PNG link, share page, alt text, and editable PowerPoint/Excel links – exactly the return-value context missing from the structured fields. It leaves out things like link persistence or rendering latency, which keeps it off a 5.

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, front-loaded with what the tool draws, then when to use it, then what comes back. No redundant restatement of the title and no filler.

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?

With no output schema, the description properly carries the return-value burden and does so concretely; the six parameters are fully documented in-schema, and the required-field contract (data only) needs no further prose. Nothing an agent needs to call this correctly is absent.

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% and the schema already documents date format, value typing, width/height defaults and the colorblind option value. The description only restates the underlying encoding rule ("one cell per day coloured by value") and adds no new information about theme, title, or brandTheme beyond what is structured.

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 specific verb and resource ("Draw a calendar heatmap") and immediately pins the shape with "GitHub-style contribution grid: one cell per day coloured by value," which distinguishes it from the generic sibling generate_heatmap_chart without needing to name it.

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?

"Use for daily activity over weeks or months" gives a clear positive selection rule tied to data granularity. It stops short of an explicit exclusion (e.g., "do not use for hourly or category-by-category matrices – use generate_heatmap_chart instead"), so the agent must infer the negative case from sibling names.

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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