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anythink-cloud

Anythink-MCP

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Charts: create

charts_create

Create and save a chart for entity records, platform usage metrics, or plan quotas, with filters and aggregation. Charts stay hidden until added to a dashboard.

Instructions

Create a chart. Check it first with charts preview. Saves a chart that plots an entity's records, a platform usage metric (see charts_platform_metrics) or a plan quota. Settings are checked first and the call is refused with the problems listed. The new chart isn't shown anywhere until you put it on a dashboard with dashboards_add_chart or dashboards_create. Returns JSON.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNoEnd of a custom time window, e.g. 2026-03-31
aggNoHow to combine the --measure values: count (default), sum, avg, min or max
fromNoStart of a custom time window, e.g. 2026-01-01
nameYesChart name
sortNoSort direction: asc or desc
typeNoChart type: column (bar chart), line, pie, area, stat, gauge or funnel
limitNoMost points or rows to plot
configNoOther chart settings as a JSON object, e.g. {"limit":10,"target":500} or funnelStages for a funnel. Typed options win over this. Setting names can be camelCase or snake_case
entityNoEntity whose records the chart plots (for an entity chart)
filterNoFilter as field:operator:value, e.g. status:eq:published. Operators: eq, ne, gt, gte, lt, lte, contains, in, exists, not_exists. Repeat for several fields. Replaces the chart's existing filters
sourceNoWhere the data comes from: entity (default), platform (usage metrics) or quota (plan limits)
targetNoGoal or warning level drawn on a stat or gauge
compareNoCompare with the previous period (true/false)
measureNoNumeric field to aggregate (needed unless --agg is count)
group_byNoField for the x axis, or the slices of a pie. A date field is bucketed with --interval
intervalNoBucket size for a date --group-by: hour, day, week, month, quarter or year
stack_byNoField that splits each bar or point into a second breakdown
timeframeNoTime window: all, 24h, 7d, 30d, 90d, 365d or custom
cumulativeNoPlot a running total (true/false)
target_modeNoWhat --target means: goal or warning
compare_daysNoHow many days back the comparison period starts (defaults to the length of the time window)
quota_metricNoPlan limit for a quota chart: storage, files, users, workflows or emails
compare_overlayNoDraw the previous period over the current one (true/false)
platform_metricNoUsage metric for a platform chart. List them with: charts platform-metrics
platform_periodNoWindow for a platform metric: day, week, month, quarter or year

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.2.37

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare the mutation/read and safety profile (readOnlyHint=false, destructiveHint=false, openWorldHint=false). The description adds valuable behavior beyond that: settings are validated and the call is refused with problems listed, and the chart stays invisible until attached to a dashboard. It doesn't detail permission requirements or rate limits.

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?

Front-loaded with the core action and then packs workflow, side-effect and return-format facts into a few tight sentences with no filler. Every sentence earns its place.

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?

For a 25-parameter mutation tool with no output schema, the description covers the essential workflow, validation/refusal behavior, and the post-create visibility constraint. It notes 'Returns JSON' but the absence of an output schema leaves return shape largely unstated, a minor gap given the richness elsewhere.

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 parameter docs are very detailed, so the schema carries the semantic load. The description adds no per-parameter meaning beyond what the schema already states, so the baseline 3 applies.

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+resource ('Create a chart') and immediately clarifies the three data sources it can plot (entity records, platform usage metric, plan quota), naming the relevant sibling charts_platform_metrics. An agent can distinguish it from charts_update/charts_preview without opening a 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 explicit workflow guidance ('Check it first with charts preview') and names where the result must go next (dashboards_add_chart or dashboards_create). It stops short of stating when NOT to use it (e.g. editing an existing chart via charts_update), so it is strong but not fully exhaustive.

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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