list_themesUse this when choosing a visual theme for a chart. Returns every available theme and guidance on where each works best.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| themes | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Output schema / properties / themes / items / properties / name / enumAdded value: +[
+ "editorial",
+ "clean",
+ "noir",
+ "loud",
+ "soft",
+ "technical"
+]Output schema / (root)Previous value: -nullNew value: +{
+ "$schema": "http://json-schema.org/draft-07/schema#",
+ "additionalProperties": false,
+ "properties": {
+ "themes": {
+ "items": {
+ "additionalProperties": false,
+ "properties": {
+ "description": {
+ "type": "string"
+ },
+ "name": {
+ "type": "string"
+ }
+ },
+ "required": [
+ "name",
+ "description"
+ ],
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "required": [
+ "themes"
+ ],
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the agent knows it's safe. The description adds that the tool returns 'guidance on where each works best,' which provides behavioral context beyond annotations. 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with usage intent, and no wasted words. Every sentence contributes meaning.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no parameters and an output schema that presumably details the return structure, the description provides complete context: what the tool does and when to use it. No gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, so the description's mention of what is returned (themes and guidance) adds value. Schema coverage is 100%, so baseline is 3, but the description compensates by explaining the output nature.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: listing visual themes for charts and providing guidance. It uses a specific verb ('Use this when choosing') and resource ('visual theme for a chart'), and distinguishes it from sibling tools like list_charts or list_marks.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Use this when choosing a visual theme for a chart,' providing a clear context of use. It does not mention when not to use or alternatives, but given the simplicity of the tool (no parameters), this is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clearly distinct purposes, but get_chart and get_chart_config are both retrieval-by-id operations that require careful reading to distinguish metadata/URLs from config. Additionally, get_preview_state is somewhat cryptic despite its description, but overall overlap is minimal.
All 13 tool names follow a consistent verb_noun pattern in lowercase snake_case (e.g., delete_chart, get_chart_config, list_themes). There are no deviations or mixed conventions, making the naming predictable and coherent.
With 13 tools, the server is well-scoped for a charting service. Each tool serves a distinct role in the workflow (reference, validation, CRUD, rendering), and the count is neither sparse nor overwhelming, fitting within the ideal 3-15 range.
The tool surface covers the full chart lifecycle: create (save_chart), read (get_chart, get_chart_config, list_charts), update (update_chart, rename_chart), and delete (delete_chart). It also includes validation, rendering, reference data (examples, marks, themes), and preview state restoration, addressing practical needs. Minor gaps like explicit draft management are indirectly handled through update_chart's safeguards.