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kdqed
by kdqed

list_data_sources

Discover available data sources to identify which ones you can query for analytics and business intelligence. Returns unique source IDs with type information for proper SQL syntax usage.

Instructions

List all available data sources. Returns a list of unique source_ids to be used for other queries. Source type is included in the source_id string. While drafting SQL queries use appropriate syntax as per source type.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The main handler function implementing the list_data_sources tool. It generates a string listing all data sources and their tables using query_utils.list_tables.
    def list_data_sources(self) -> str:
        """
        List all available data sources.
        Returns a list of unique source_ids to be used for other queries.
        Source type is included in the source_id string.
        While drafting SQL queries use appropriate syntax as per source type.
        """
        try:
            if not self.data_sources:
                return "No data sources available. Add data sources."
            
            result = "Available data sources:\n\n"
            for source_id in self.data_sources:
                tables = query_utils.list_tables(
                    self.data_sources[source_id]
                )
                if type(tables) is list:
                    tables = ', '.join(tables)
                result += f"- {source_id}\nHas tables: {tables}\n"
                
            return result
            
        except Exception as e:
            return str(e)
  • The list_data_sources tool is registered in the Core class's tools list within its __init__ method.
    self.tools = [
        self.list_data_sources,
        self.describe_table,
        self.run_query,
    ]
  • ZaturnTools class registers tools by including those from core.Core, which contains list_data_sources.
    class ZaturnTools:
    
        def __init__(self, data_sources):
            self.tools = [
                *core.Core(data_sources).tools,
                *visualizations.Visualizations(data_sources).tools,
            ]
Behavior3/5

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

With no annotations provided, the description carries full burden. It discloses that the tool returns source_ids with type information embedded, which is useful behavioral context. However, it doesn't mention potential limitations like pagination, rate limits, authentication requirements, or whether the list is static vs. dynamic. The description adds value but leaves gaps in behavioral disclosure.

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 concise with three sentences that each serve distinct purposes: stating the action, explaining the output format, and providing usage context. It's front-loaded with the core purpose. The SQL syntax mention could be slightly more integrated, but overall it's efficient with minimal waste.

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 has 0 parameters, 100% schema coverage, and an output schema exists, the description provides good contextual completeness. It explains the purpose, output format, and practical usage for query preparation. For a simple listing tool with structured output schema, the description covers the essential context without needing to detail return values that the output schema will define.

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 tool has 0 parameters with 100% schema description coverage (empty schema). The description appropriately doesn't discuss parameters since none exist. It instead focuses on output semantics: 'Returns a list of unique source_ids' and 'Source type is included in the source_id string.' This provides good context about what the tool returns despite no input parameters.

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: 'List all available data sources' with the specific verb 'List' and resource 'data sources'. It distinguishes from siblings by focusing on metadata listing rather than data visualization or query execution. However, it doesn't explicitly contrast with specific sibling tools like 'describe_table' which might also provide metadata.

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 provides implied usage guidance: 'Returns a list of unique source_ids to be used for other queries' suggests this tool should be used before querying data. 'While drafting SQL queries use appropriate syntax as per source type' implies it helps with query preparation. However, it lacks explicit when-to-use vs. when-not-to-use instructions or named alternatives among siblings.

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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