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dlt

dlt

Official
by dlt-hub
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Python
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display_schema

Generates a mermaid diagram to visualize the schema of a specified dlt pipeline.

Instructions

Generate a mermaid diagram to represent the pipeline schema

pipeline_name: name of the pipeline hide_columns: when True, the columns are hidden

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": {
      "pipeline_name": "<pipeline_name>"
    },
    "name": "display_schema"
  }
}

Input Schema

NameRequiredDescriptionDefault
pipeline_nameYes
hide_columnsNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

B3.4/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It only states the output is a mermaid diagram but does not disclose whether it is read-only, if it requires specific permissions, or any side effects. Minimal behavioral context.

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, with two lines for the action and parameter details. No wasted words, front-loaded with the main purpose. Could be improved by moving parameter details into the schema descriptions.

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 has an output schema (mermaid diagram), the description does not need to explain return values. However, it lacks context about prerequisites or how this tool fits into the pipeline ecosystem. Leaves some ambiguity about usage.

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 0%, but the description adds basic meaning: pipeline_name is 'name of the pipeline' (redundant), hide_columns is explained as 'when True, the columns are hidden' (adds value). However, the compensation is marginal for the lack of schema descriptions.

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 generates a mermaid diagram for the pipeline schema. Verb 'generate' and resource 'pipeline schema' are specific, and it distinguishes from siblings like get_table_schema (textual schema) and list_pipelines (list names).

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 omits when to use this tool versus alternatives like get_table_schema or execute_sql_query. No exclusions or context for selecting this tool over siblings are given, though the action of generating a visual diagram is implied.

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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