makechartswithai
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| MAKECHARTSWITHAI_API_KEY | Yes | Your API key from makechartswithai.online/mcp | |
| MAKECHARTSWITHAI_BACKEND_URL | No | Backend URL (defaults to https://makechartswithai.online) | https://makechartswithai.online |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| recommend_chartsA | Analyzes dataset structure or user prompt to recommend suitable chart types, confidence scores, and column classifications (requiredColumns, optionalColumns, unnecessaryColumns). |
| generate_chartA | Generates a production-ready chart specification and returns it in the requested format (share_link, svg, or png). Engine selection is automatically determined by chart type. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 2 tools
recommend_charts and generate_chart have clearly distinct roles: one analyzes the dataset and suggests chart types, the other produces the actual chart artifact. There is no functional overlap, so an agent can confidently select the right tool.
Both tools follow the same verb_noun pattern with clear, action-first names. recommend_charts and generate_chart are predictable and easy to remember.
At only two tools, the server feels thin even though each tool covers an essential step. It is a minimal pipeline rather than a full-featured toolkit, which puts it in the borderline range.
The core recommend-then-generate workflow is covered with no dead ends. The main gap is lack of explicit refinement editing tools, but an agent can call generate again with adjusted parameters.