preset-mcp

validate_dashboard

Validate all charts on a dashboard and return aggregate statuses to identify issues.

Instructions

Validate all charts on a dashboard and return aggregate statuses.

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": "validate_dashboard"
  }
}

Input Schema

Name	Required	Default
`dashboard_id`	Yes
`row_limit`	No
`force`	No
`response_mode`	No	standard

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.7.2Jun 8, 2026

TDQS

B3.1/5.0

Behavior2/5

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

No annotations are provided, and the description does not disclose behavioral traits such as whether the tool is read-only, modifies data, requires authentication, or how it handles errors. The term 'aggregate statuses' is vague without explaining what statuses mean or how they are computed.

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 a single sentence that is front-loaded with the key verb and resource, containing no unnecessary words. It efficiently conveys the core functionality.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite having an output schema, the description is too brief. It does not explain validation behavior, interpretation of results, or edge cases. For a tool with 4 parameters and no annotations, the description should provide more context on when to use different parameter configurations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 4 parameters (dashboard_id, row_limit, force, response_mode) with 0% schema description coverage. The description does not explain any parameter's purpose or behavior, leaving the agent to guess. For example, 'force' and 'response_mode' are not mentioned, nor is the impact of row_limit.

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 the tool validates all charts on a dashboard and returns aggregate statuses, specifying the verb (validate), resource (all charts on a dashboard), and outcome. This distinguishes it from sibling tools like validate_chart (single chart) and validate_dashboard_render (render-specific).

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

Usage Guidelines3/5

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

The description implies usage for validating all charts on a dashboard, but it does not explicitly mention when not to use it or suggest alternatives. While the sibling list provides context (e.g., validate_chart for single charts), the description itself lacks guidance on prerequisites or decomposition strategies.

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

Deploy Server