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Privatrak

by com.privatrak
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Dashboard

get_dashboard
Read-onlyIdempotent

One dashboard and its widgets. Each widget's query_config is an insights query: pass its fields to run_insights_query to get the widget's numbers (for a single value, visualization number, with compare_previous). Widgets are what the team chose to watch, which makes them a good place to start.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "dashboard_id": "<dashboard_id>"
    },
    "name": "get_dashboard"
  }
}

Input Schema

NameRequiredDescriptionDefault
projectNoProject ID, from list_projects. Required when this connection covers more than one project; may be left out when it covers one (and with an API key, which belongs to one project).
dashboard_idYesDashboard ID, from list_dashboards.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already establish read-only, idempotent, non-open-world behavior. The description adds useful behavioral context by explaining that the result includes widgets and that each widget has a query_config that can be fed into run_insights_query.

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?

The description is short, front-loaded, and every sentence contributes either scope, workflow guidance, or usage framing. There is no redundant restatement of schema or annotations.

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 two-parameter read-only tool with no output schema, the description sufficiently explains what is returned at a high level and how widgets relate to run_insights_query. It does not detail dashboard fields or error cases, but those are secondary for correct invocation.

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%, so both dashboard_id and project are already well documented in the schema. The description adds no parameter-specific meaning beyond that structured documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description identifies the specific resource and scope: a single dashboard plus its widgets. The verb is implicit rather than explicit, and it does not name list_dashboards as the sibling for discovering dashboard IDs, so it is clear but not fully differentiated.

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?

It gives a concrete downstream usage path: widget query_config fields should be passed to run_insights_query to retrieve widget numbers. It also frames dashboards as a good starting point, though it does not explicitly state when not to use this tool versus list_dashboards or get_overview.

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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