preset-mcp

get_dashboard

Retrieve details for a specific dashboard by ID. Choose compact, standard, or full response mode to control verbosity.

Instructions

Get detail for a single dashboard.

Use list_dashboards first to find valid IDs. Use response_mode to control verbosity — dashboards can contain large position_json and json_metadata blobs.

Args: dashboard_id: Numeric dashboard ID response_mode: 'compact' (id+title), 'standard' (key fields, no position_json), or 'full' (raw API response). Default: full.

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

Name	Required	Description	Default
`dashboard_id`	Yes
`response_mode`	No		full

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.7.2Jun 8, 2026

TDQS

A4.1/5.0

Behavior3/5

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

With no annotations, the description carries full burden. It mentions that dashboards can contain large blobs and explains response_mode, but lacks details on error handling, permissions, or side effects. Adequate but not exhaustive.

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?

The description is concise and front-loaded: first sentence states purpose, then usage tip, then parameter details. No unnecessary words, though slightly repetitive in listing modes.

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?

Given the tool has an output schema (not shown) and few parameters, the description provides sufficient context: prerequisite, parameter details, and verbosity control. It doesn't need to detail return values.

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?

Despite 0% schema coverage, the description adds meaning beyond the schema: dashboard_id is numeric, and response_mode enum values are explained in context (compact, standard, full) with behavioral implications.

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 clearly states 'Get detail for a single dashboard,' specifying the action and resource. It distinguishes from sibling tools like list_dashboards and other get_* tools.

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?

The description advises to 'Use list_dashboards first to find valid IDs,' which is explicit prerequisite guidance. It also explains the response_mode options to control verbosity, helping the agent decide when to use the tool appropriately.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server