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MCP Trino Server

by alaturqua

show_partitions

Display partition details for Iceberg tables in Trino to analyze data distribution and optimize query performance.

Instructions

Show Iceberg table partitions

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
catalogYescatalog name
schema_nameYesschema name
tableYesThe name of the table

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • MCP tool handler for 'show_partitions' - decorated with @mcp.tool(), defines input schema via Field parameters, contains docstring with return info, and delegates to client.show_partitions()
    @mcp.tool(description="Show Iceberg table partitions")
    def show_partitions(
        catalog: str = Field(description="catalog name "),
        schema_name: str = Field(description="schema name "),
        table: str = Field(description="The name of the table"),
    ) -> str:
        """Show Iceberg table partitions.
    
        The partitions table contains:
        - partition: Mapping of partition column names to values
        - record_count: Number of records in partition
        - file_count: Number of files in partition
        - total_size: Total size of files in partition
        - data: Partition range metadata with min/max values and null/nan counts
    
        Args:
            catalog: catalog name
            schema_name: schema name
            table: The name of the table
    
        Returns:
            str: JSON-formatted table partitions
        """
        return client.show_partitions(catalog, schema_name, table)
  • Actual implementation in TrinoClient class - constructs the table identifier with $partitions suffix and executes the query to fetch Iceberg table partition metadata
    def show_partitions(self, table: str, catalog: str, schema: str) -> str:
        """Show Iceberg table partitions.
    
        The partitions table contains:
        - partition: ROW(...) - Mapping of partition column names to values
        - record_count: BIGINT - Number of records in partition
        - file_count: BIGINT - Number of files in partition
        - total_size: BIGINT - Total size of files in partition
        - data: ROW(...) - Partition range metadata with min/max values and null/nan counts
    
        Args:
            table: The name of the table
            catalog: Optional catalog name (defaults to configured catalog)
            schema: Optional schema name (defaults to configured schema)
    
        Returns:
            str: JSON-formatted string containing table partitions
        """
        catalog = catalog or self.config.catalog
        schema = schema or self.config.schema
        if not catalog or not schema:
            raise CatalogSchemaError
        table_identifier = f"{catalog}.{schema}.{table}$partitions"
        query = 'SELECT * FROM "{}"'
        return self.execute_query(query.format(table_identifier))
  • Input schema definition for 'show_partitions' tool - uses pydantic Field to define catalog, schema_name, and table parameters with descriptions
    @mcp.tool(description="Show Iceberg table partitions")
    def show_partitions(
        catalog: str = Field(description="catalog name "),
        schema_name: str = Field(description="schema name "),
        table: str = Field(description="The name of the table"),
    ) -> str:
Behavior2/5

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 but fails to state whether this is a read-only operation, its performance characteristics, or required permissions. The verb 'Show' implies safety but does not explicitly confirm non-destructive behavior or idempotency.

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 consists of a single three-word phrase with no redundant or wasted text, earning high marks for efficiency. However, it borders on being too terse for the tool's complexity, lacking any structural elaboration that might aid comprehension.

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 existence of an output schema, the description appropriately omits return value details, meeting minimum viability for a metadata inspection tool. However, it lacks contextual guidance regarding sibling tool differentiation that would help an agent select the correct metadata view.

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?

The input schema has 100% description coverage for all three parameters (catalog, schema_name, table), establishing a baseline score. The description adds no additional parameter context, but none is needed given the comprehensive schema documentation.

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 uses a clear verb ('Show') and identifies the specific resource ('Iceberg table partitions'), distinguishing it from siblings like show_files or show_snapshots. However, it stops short of clarifying what partition information is returned (e.g., specs, values, or metadata), preventing a top score.

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?

The description provides no guidance on when to use this tool versus similar metadata inspection tools like show_files or show_entries. There are no prerequisites mentioned, nor any indication of performance implications when querying large tables.

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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