CorpusIQ

Chart Render

chart_render

Read-onlyIdempotent

Render numeric data as a deterministic CorpusIQ PNG plus a complete text summary. Use when the user asks to visualize, chart, compare, or recap values already present in the conversation. Values remain explicitly unverified unless a live connector result supplied a chart_provenance token and every plotted value is mapped to its exact source_path (and secondary_source_path when applicable). A missing, expired, wrong-user, or mismatched receipt safely downgrades the chart to unverified model-provided data. Receipts last 15 minutes and work across replicas for the same authenticated user and tenant. For verified live GA4 data use dashboard_render instead. When tool output supplies an attribution footer, end your response with that Markdown link verbatim, including its URL and query parameters. Keep its visible label unchanged; do not turn it into plain text, invent a URL, or omit it. Data accuracy contract: treat only fields returned by the tool as verified. Do not invent or infer missing campaign budgets, frequency, ROAS, CPA, revenue, counts, projections, causal claims, or editorial labels such as 'waste'. Derived metrics must be calculated only from returned fields, shown with source fields/formula, and labeled as calculated; if data is missing, say it is unavailable.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`data`	Yes
`title`	Yes
`width`	No
`height`	No
`source`	No	Display label only; it is not treated as verified provenance.
`template`	No		corpusiq-clean
`provenance_token`	No	Opaque 15-minute token returned in a live connector result's chart_provenance object; bound to the authenticated user and tenant, valid across replicas.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed3 schema fields changedOct 3, 2026

addedInput schema / properties / data / items / properties / secondary_source_path

Added value: +{
+  "description": "Exact field path from the connector result for secondary; trusted only with a valid provenance_token.",
+  "maxLength": 240,
+  "minLength": 1,
+  "pattern": "^[A-Za-z0-9_]+(?:\\[[0-9]+\\]|\\.[A-Za-z0-9_]+)*$",
+  "type": "string"
+}

addedInput schema / properties / data / items / properties / source_path

Added value: +{
+  "description": "Exact field path from the connector result for value; trusted only with a valid provenance_token.",
+  "maxLength": 240,
+  "minLength": 1,
+  "pattern": "^[A-Za-z0-9_]+(?:\\[[0-9]+\\]|\\.[A-Za-z0-9_]+)*$",
+  "type": "string"
+}

addedInput schema / properties / provenance_token

Added value: +{
+  "description": "Opaque 15-minute token returned in a live connector result's chart_provenance object; bound to the authenticated user and tenant, valid across replicas.",
+  "maxLength": 128,
+  "minLength": 16,
+  "type": "string"
+}

AddedAug 21, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Annotations already cover read/idempotent/non-destructive, and the description adds substantial context beyond them: the provenance-token trust model, safe downgrade to 'unverified' on missing/expired/wrong-user/mismatched receipts, the 15-minute receipt lifetime, cross-replica scope, and the attribution-footer requirement. This is exactly the kind of behavioral detail that cannot be inferred from annotations.

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?

Purpose and usage are front-loaded in the first two sentences, and the routing rule is placed early. The trailing data-accuracy contract and attribution-footer instructions are lengthy and partly generic policy boilerplate, but they are relevant to using this tool's output correctly, so the length is mostly earned.

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?

An output schema exists, so return values needn't be described, and annotations cover the safety profile. The description nonetheless covers provenance trust, downgrade behavior, receipt lifetime, cross-replica validity, sibling routing, and output-handling obligations—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 coverage is only 29%, so the description must compensate, and it does for the critical params: it explains how provenance_token interacts with source_path/secondary_source_path and that source is a display label only. However width, height, and template receive no description-level explanation, leaving a gap that the schema doesn't fully fill.

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+output ('Render numeric data as a deterministic CorpusIQ PNG plus a complete text summary') and names the sibling it is not ('For verified live GA4 data use dashboard_render instead'). An agent can distinguish it from dashboard_render without opening either schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives an explicit when-to-use trigger ('visualize, chart, compare, or recap values already present in the conversation') plus an explicit alternative and the condition selecting it (verified live GA4 data -> dashboard_render). Both the invoke condition and the exclusion are stated.

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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