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alaturqua

MCP Trino Server

by alaturqua

show_refs

List branches and tags for Iceberg tables to track versions, manage snapshots, and maintain data lineage in Trino.

Instructions

Show Iceberg table references (branches and tags)

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_refs'. Decorated with @mcp.tool(), this function receives the tool call, validates inputs using Pydantic Fields, and delegates to the Trino client. Returns JSON-formatted table references.
    @mcp.tool(description="Show Iceberg table references (branches and tags)")
    def show_refs(
        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 references (branches and tags).
    
        The refs table contains:
        - name: Name of the reference
        - type: Type of reference (BRANCH or TAG)
        - snapshot_id: ID of referenced snapshot
        - max_reference_age_in_ms: Max age before reference expiry
        - min_snapshots_to_keep: Min snapshots to keep (branches only)
        - max_snapshot_age_in_ms: Max snapshot age in branch
    
        Args:
            catalog: catalog name
            schema_name: schema name
            table: The name of the table
    
        Returns:
            str: JSON-formatted table references
        """
        return client.show_refs(catalog, schema_name, table)
  • Core implementation of show_refs in the TrinoClient class. Constructs and executes a SQL query against Trino's Iceberg $refs metadata table. Handles default catalog/schema resolution and returns JSON-formatted results.
    def show_refs(self, table: str, catalog: str, schema: str) -> str:
        """Show Iceberg table references (branches and tags).
    
        The refs table contains:
        - name: VARCHAR - Name of the reference
        - type: VARCHAR - Type of reference (BRANCH or TAG)
        - snapshot_id: BIGINT - ID of referenced snapshot
        - max_reference_age_in_ms: BIGINT - Max age before reference expiry
        - min_snapshots_to_keep: INTEGER - Min snapshots to keep (branches only)
        - max_snapshot_age_in_ms: BIGINT - Max snapshot age in branch
    
        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 references
        """
        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}$refs"
        query = 'SELECT * FROM "{}"'
        return self.execute_query(query.format(table_identifier))
  • Input schema definition for show_refs tool using Pydantic Field. Defines three string parameters: catalog, schema_name, and table with descriptive metadata for MCP tool discovery.
    @mcp.tool(description="Show Iceberg table references (branches and tags)")
    def show_refs(
        catalog: str = Field(description="catalog name "),
        schema_name: str = Field(description="schema name "),
        table: str = Field(description="The name of the table"),
    ) -> str:
  • src/server.py:468-468 (registration)
    MCP tool registration point. The @mcp.tool() decorator registers show_refs with the FastMCP server, making it discoverable and callable via the Model Context Protocol.
    @mcp.tool(description="Show Iceberg table references (branches and tags)")
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 of behavioral disclosure. While 'Show' implies a read-only operation, the description does not confirm this, nor does it indicate performance characteristics, pagination behavior, or the format of the returned references despite the operation potentially returning complex branch/tag metadata.

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 redundant text. However, it borders on under-specification given the lack of annotations and the tool's place among numerous sibling utilities; an additional sentence clarifying usage context would improve utility without sacrificing clarity.

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 simple 3-parameter schema and the existence of an output schema, the description meets minimum viability by identifying what the tool retrieves. However, the absence of annotations combined with minimal behavioral detail leaves gaps in contextual understanding that the description could reasonably address.

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?

With 100% schema description coverage across all three parameters (catalog, schema_name, table), the schema adequately documents the inputs. The description adds no additional parameter context, meeting the baseline expectation for well-schematized tools but not enhancing understanding beyond the structured definitions.

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 specific verb ('Show') and clearly identifies the resource ('Iceberg table references'), distinguishing it from sibling tools by specifying the exact metadata returned ('branches and tags'). However, it stops short of being exemplary by not clarifying the Iceberg-specific context or explicitly contrasting with similar tools like show_snapshots.

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 the many available alternatives (show_snapshots, show_table_history, show_entries). There are no prerequisites mentioned, nor any indication of when this query is preferred over others.

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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