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Insight Digger MCP

Insight Digger MCP

by sandsiv
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

get_charts_data

Fetch data for multiple charts using cached configurations and authentication from Insight Digger MCP to prepare for dashboard visualization.

Instructions

šŸ”’ [Requires Authentication] Fetch data for multiple charts. This step is granular. šŸ”„ Auto-Cached: 'chartConfigs' is automatically provided from the create_dashboard step, and 'apiUrl'/'jwtToken' from authentication. You typically don't need to provide any parameters for this tool. Returns a summary of fetched charts (chart names). The full data is cached for the next step.

āš ļø Please authenticate first by calling the setup_authentication tool above. This tool will become fully functional after authentication.

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": {
      "chartConfigs": "<chartConfigs>"
    },
    "name": "get_charts_data"
  }
}

Input Schema

NameRequiredDescriptionDefault
chartConfigsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.4/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: authentication requirements ('šŸ”’ [Requires Authentication]'), caching behavior ('šŸ”„ Auto-Cached'), parameter handling ('automatically provided'), and output behavior ('Returns a summary... The full data is cached for the next step'). This covers critical operational aspects beyond basic functionality.

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 appropriately sized and front-loaded with key information (authentication, purpose, caching). It uses formatting (bold, emojis) for emphasis, but includes some redundancy (e.g., repeating authentication instructions) and could be slightly more streamlined without losing clarity.

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's complexity (involves authentication, caching, and dependencies), no annotations, and no output schema, the description is largely complete. It covers purpose, prerequisites, behavioral traits, and parameter context. However, it lacks details on error handling, rate limits, or the exact format of the returned summary, which could be useful for full contextual understanding.

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?

The input schema has 1 parameter with 0% description coverage, and the description compensates well. It explains that 'chartConfigs' is 'automatically provided from the create_dashboard step' and that 'You typically don't need to provide any parameters,' clarifying the parameter's source and usage context. However, it doesn't detail the structure or content of 'chartConfigs' (e.g., what it contains), leaving some ambiguity.

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 clearly states the tool's purpose: 'Fetch data for multiple charts' and 'Returns a summary of fetched charts (chart names).' It specifies the verb (fetch) and resource (data for charts), but doesn't explicitly distinguish it from sibling tools like 'analyze_charts' or 'create_dashboard' beyond mentioning the caching relationship with 'create_dashboard.'

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?

The description provides explicit usage guidance: it states 'You typically don't need to provide any parameters for this tool' due to auto-caching, specifies prerequisites ('Please authenticate first by calling the `setup_authentication` tool'), and indicates the tool's dependency on 'create_dashboard' for 'chartConfigs.' This clearly defines when and how to use it versus alternatives.

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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