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Insight Digger MCP

Insight Digger MCP

by sandsiv
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

analyze_source_structure

Analyze data source structure to identify columns and relationships, enabling formulation of analytical questions for enterprise data workflows.

Instructions

šŸ”’ [Requires Authentication] Fetches and analyzes the structure of a given data source. This is a key step after selecting a source. šŸ”„ Auto-Cached: apiUrl and jwtToken are automatically provided from authentication. Only provide the 'sourceId' parameter (from the previous list_sources step). Returns detailed column analysis to help formulate analytical questions.

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": {
      "sourceId": "<sourceId>"
    },
    "name": "analyze_source_structure"
  }
}

Input Schema

NameRequiredDescriptionDefault
sourceIdYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.1/5.0
Behavior4/5

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

Since no annotations are provided, the description carries the full burden. It discloses key behavioral traits: authentication requirement ('Requires Authentication'), auto-caching of parameters ('šŸ”„ Auto-Cached'), and that it's a read operation (implied by 'fetches and analyzes'). However, it lacks details on rate limits, error handling, or specific output format, which could enhance transparency.

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, starting with key info (authentication, purpose) and using emojis for emphasis. However, some redundancy exists (e.g., repeating authentication instructions), and it could be more streamlined without losing essential details.

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

Completeness3/5

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

Given the tool's complexity (analysis operation), lack of annotations, and no output schema, the description is somewhat complete but has gaps. It covers authentication, usage context, and parameter guidance but lacks details on return values (e.g., what 'detailed column analysis' includes) and potential side effects, which would improve completeness for an agent.

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?

With 0% schema description coverage and 1 parameter, the description compensates well by explaining that 'sourceId' is 'from the previous list_sources step' and that only this parameter should be provided. This adds meaningful context beyond the schema, though it could specify the format or constraints of 'sourceId' for full clarity.

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 'fetches and analyzes the structure of a given data source' and mentions it 'returns detailed column analysis,' which specifies the verb (fetch and analyze) and resource (data source structure). It distinguishes from siblings like 'list_sources' by focusing on analysis rather than listing, though it could be more explicit about how it differs from tools like 'analyze_charts' or 'generate_config.'

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 explicitly states when to use this tool: 'after selecting a source' and 'Please authenticate first by calling the `setup_authentication` tool above.' It also mentions an alternative ('list_sources' for selecting a source) and prerequisites, providing clear guidance on context and exclusions.

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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