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Analytics dashboard

view_analytics
Read-onlyIdempotent

Open an interactive analytics dashboard for a date range — totals, status breakdown, per-platform stats, and daily post counts. Use this when the user wants an interactive analytics dashboard — prefer over get_analytics for a visual view.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNoEnd date (ISO). Defaults to today.
fromNoStart date (ISO, e.g. 2025-01-01). Defaults to 30 days ago.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNo
fromNo
summaryNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering risks. The description additionally clarifies the tool's behavior (interactive dashboard) and what the view contains, which goes beyond the structured annotations. It does not mention any auth/rate constraints, but these are not necessary given the pervasive read-only safety profile and the annotation coverage.

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?

Two tight sentences that lead with the precise action, list the dashboard content, and give selection guidance. There is no filler and each 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?

The description gives sufficient context for a tool that already has an output schema and annotations for side-effect behavior. It clearly conveys what the dashboard includes and when to use it. The 'interactive' nature is described with enough clarity for typical agent invocation; a slightly deeper note about return format would be a minor embellishment, but output schema covers that.

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?

The input schema fully describes both parameters with dates and defaults (100% coverage), so the description adds no new parameter semantics; it only repeats the concept of a date range. With the schema doing the heavy lifting, baseline 3 is right.

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?

The description explicitly says 'Open an interactive analytics dashboard' and lists the contents: totals, status breakdown, per-platform stats, daily post counts. It also distinguishes itself from get_analytics by noting that this is the visual alternative. This fully disambiguates it from sibling tools without ambiguity.

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?

It provides a clear decision rule: 'Use this when the user wants an interactive analytics dashboard — prefer over get_analytics for a visual view.' This explicitly states when to select it and names the actual alternative, so an agent can decide without inspecting other tools.

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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