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dlt

dlt

Official
by dlt-hub
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Python
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list_tables

Specify a pipeline name to retrieve its complete list of available tables. Use this to discover and manage tables in your dlt pipeline.

Instructions

List all available tables in the specified pipeline.

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": "list_tables"
  }
}

Input Schema

NameRequiredDescriptionDefault
pipeline_nameYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

B3.1/5.0
Behavior2/5

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

With no annotations, the description carries full burden for behavioral disclosure. It only states the action without addressing side effects, permissions, or whether it is read-only. For a listing operation, this minimal information may be sufficient, but it lacks transparency about required authorization or result format.

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 a single, efficient sentence with no filler. It is appropriately sized for a simple tool, but could incorporate more detail (e.g., output explanation) within the same length.

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 low complexity and presence of an output schema, the description provides basic completeness for a listing operation. However, it omits context such as what 'available' means (e.g., tables the user has access to) and does not describe the output structure, which the schema likely covers but is not explicitly referenced.

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

Parameters2/5

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

The schema has 0% description coverage, and the description only implicitly refers to the pipeline_name parameter via 'in the specified pipeline'. It does not explain what constitutes a valid pipeline name, where to obtain it, or any constraints. The description adds minimal semantic value beyond the parameter's existence.

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 verb 'List', resource 'tables', and scope 'in the specified pipeline'. It is specific and distinct from sibling tools like list_pipelines (which lists pipelines) and get_table_schema (which gets schema of a specific table).

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives such as get_table_schema or display_schema. The description does not mention exclusions, prerequisites, or typical use cases, leaving the agent to infer context from the tool name alone.

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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