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get_object_details

Retrieve detailed metadata for database objects like tables, views, sequences, or extensions in Postgres MCP by specifying schema and object names for accurate insights.

Instructions

Show detailed information about a database object

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
object_nameYesObject name
object_typeNoObject type: 'table', 'view', 'sequence', or 'extension'table
schema_nameYesSchema name

Implementation Reference

  • The handler function for the 'get_object_details' tool. It retrieves detailed information about database objects (tables, views, sequences, extensions) including columns, data types, nullability, defaults, constraints, and indexes by executing appropriate SQL queries against information_schema and pg_catalog.
    @mcp.tool(description="Show detailed information about a database object")
    async def get_object_details(
        schema_name: str = Field(description="Schema name"),
        object_name: str = Field(description="Object name"),
        object_type: str = Field(description="Object type: 'table', 'view', 'sequence', or 'extension'", default="table"),
    ) -> ResponseType:
        """Get detailed information about a database object."""
        try:
            sql_driver = await get_sql_driver()
    
            if object_type in ("table", "view"):
                # Get columns
                col_rows = await SafeSqlDriver.execute_param_query(
                    sql_driver,
                    """
                    SELECT column_name, data_type, is_nullable, column_default
                    FROM information_schema.columns
                    WHERE table_schema = {} AND table_name = {}
                    ORDER BY ordinal_position
                    """,
                    [schema_name, object_name],
                )
                columns = (
                    [
                        {
                            "column": r.cells["column_name"],
                            "data_type": r.cells["data_type"],
                            "is_nullable": r.cells["is_nullable"],
                            "default": r.cells["column_default"],
                        }
                        for r in col_rows
                    ]
                    if col_rows
                    else []
                )
    
                # Get constraints
                con_rows = await SafeSqlDriver.execute_param_query(
                    sql_driver,
                    """
                    SELECT tc.constraint_name, tc.constraint_type, kcu.column_name
                    FROM information_schema.table_constraints AS tc
                    LEFT JOIN information_schema.key_column_usage AS kcu
                      ON tc.constraint_name = kcu.constraint_name
                     AND tc.table_schema = kcu.table_schema
                    WHERE tc.table_schema = {} AND tc.table_name = {}
                    """,
                    [schema_name, object_name],
                )
    
                constraints = {}
                if con_rows:
                    for row in con_rows:
                        cname = row.cells["constraint_name"]
                        ctype = row.cells["constraint_type"]
                        col = row.cells["column_name"]
    
                        if cname not in constraints:
                            constraints[cname] = {"type": ctype, "columns": []}
                        if col:
                            constraints[cname]["columns"].append(col)
    
                constraints_list = [{"name": name, **data} for name, data in constraints.items()]
    
                # Get indexes
                idx_rows = await SafeSqlDriver.execute_param_query(
                    sql_driver,
                    """
                    SELECT indexname, indexdef
                    FROM pg_indexes
                    WHERE schemaname = {} AND tablename = {}
                    """,
                    [schema_name, object_name],
                )
    
                indexes = [{"name": r.cells["indexname"], "definition": r.cells["indexdef"]} for r in idx_rows] if idx_rows else []
    
                result = {
                    "basic": {"schema": schema_name, "name": object_name, "type": object_type},
                    "columns": columns,
                    "constraints": constraints_list,
                    "indexes": indexes,
                }
    
            elif object_type == "sequence":
                rows = await SafeSqlDriver.execute_param_query(
                    sql_driver,
                    """
                    SELECT sequence_schema, sequence_name, data_type, start_value, increment
                    FROM information_schema.sequences
                    WHERE sequence_schema = {} AND sequence_name = {}
                    """,
                    [schema_name, object_name],
                )
    
                if rows and rows[0]:
                    row = rows[0]
                    result = {
                        "schema": row.cells["sequence_schema"],
                        "name": row.cells["sequence_name"],
                        "data_type": row.cells["data_type"],
                        "start_value": row.cells["start_value"],
                        "increment": row.cells["increment"],
                    }
                else:
                    result = {}
    
            elif object_type == "extension":
                rows = await SafeSqlDriver.execute_param_query(
                    sql_driver,
                    """
                    SELECT extname, extversion, extrelocatable
                    FROM pg_extension
                    WHERE extname = {}
                    """,
                    [object_name],
                )
    
                if rows and rows[0]:
                    row = rows[0]
                    result = {"name": row.cells["extname"], "version": row.cells["extversion"], "relocatable": row.cells["extrelocatable"]}
                else:
                    result = {}
    
            else:
                return format_error_response(f"Unsupported object type: {object_type}")
    
            return format_text_response(result)
        except Exception as e:
            logger.error(f"Error getting object details: {e}")
            return format_error_response(str(e))
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. It states this is a read operation ('show'), but doesn't mention permissions needed, rate limits, whether it's safe or destructive, or what format the detailed information includes. For a tool with zero annotation coverage, this is inadequate.

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 with no wasted words. It's front-loaded with the core purpose, making it easy to parse 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 explain what 'detailed information' entails, potential return formats, or error conditions. For a tool with no structured behavioral data, this leaves significant gaps.

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 100%, so the schema fully documents all three parameters. The description doesn't add any parameter-specific details beyond what the schema provides, such as examples or constraints, resulting in the baseline score.

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 verb 'show' and resource 'detailed information about a database object', making the purpose understandable. However, it doesn't differentiate from sibling tools like 'list_objects' or 'explain_query' which might also provide object information, so it misses the highest 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?

No guidance is provided on when to use this tool versus alternatives like 'list_objects' or 'explain_query'. The description is generic and doesn't mention prerequisites, context, or exclusions, leaving the agent with minimal usage direction.

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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