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ryft-io

IcebergMCP

by ryft-io

get_iceberg_tables

Retrieve a list of Apache Iceberg tables from a specified namespace to access and manage data lakehouse metadata through the IcebergMCP server.

Instructions

Provides a list of iceberg tables from the Iceberg catalog for a given namespace

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
namespaceYes

Implementation Reference

  • The handler function for the 'get_iceberg_tables' tool, decorated with @mcp.tool() for registration. It retrieves the list of tables in the specified namespace from the Iceberg Glue catalog and returns their names as a newline-separated string.
    @mcp.tool()
    def get_iceberg_tables(namespace: str) -> str:
        """Provides a list of iceberg tables from the Iceberg catalog for a given namespace"""
        catalog = get_catalog()
        tables = catalog.list_tables(namespace)
        return "\n".join(t[1] for t in tables)
  • Helper function used by the tool to initialize and return the AWS Glue Catalog instance for accessing Iceberg tables.
    def get_catalog() -> GlueCatalog:
        try:
            session = boto3.Session(profile_name=iceberg_config.profile_name)
            credentials = session.get_credentials().get_frozen_credentials()
    
            catalog = GlueCatalog(
                "glue",
                **{
                    "client.access-key-id": credentials.access_key,
                    "client.secret-access-key": credentials.secret_key,
                    "client.session-token": credentials.token,
                    "client.region": iceberg_config.region,
                },
            )
        except Exception as e:
            logger.error(f"Error creating AWS connection: {str(e)}")
            raise
        return catalog
  • The @mcp.tool() decorator registers the get_iceberg_tables function as an MCP tool.
    @mcp.tool()
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 states the tool provides a list but doesn't disclose behavioral traits such as whether it's read-only, pagination behavior, error handling, or rate limits. The description is minimal and lacks essential operational context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's function without unnecessary words. It is appropriately sized and front-loaded, making it easy to grasp quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of annotations and output schema, the description is incomplete. It doesn't cover behavioral aspects, return format, or error conditions. For a tool with one parameter but no schema descriptions, more context is needed to be fully helpful.

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 meaning by specifying that the namespace parameter is used to filter iceberg tables. However, it doesn't explain what a namespace is, its format, or examples, leaving gaps in understanding beyond the basic schema.

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 action ('Provides a list') and resource ('iceberg tables from the Iceberg catalog'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like get_namespaces or get_table_schema, which would require a more specific scope comparison.

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 implies usage for listing tables in a namespace but provides no guidance on when to use this tool versus alternatives like get_namespaces or get_table_schema. There are no explicit when/when-not instructions or prerequisites mentioned.

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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