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analyze_db_health

Analyze PostgreSQL database health by checking indexes, connections, vacuum status, sequences, replication, buffer cache, and constraints to identify performance issues.

Instructions

Analyzes database health. Here are the available health checks:

  • index - checks for invalid, duplicate, and bloated indexes

  • connection - checks the number of connection and their utilization

  • vacuum - checks vacuum health for transaction id wraparound

  • sequence - checks sequences at risk of exceeding their maximum value

  • replication - checks replication health including lag and slots

  • buffer - checks for buffer cache hit rates for indexes and tables

  • constraint - checks for invalid constraints

  • all - runs all checks You can optionally specify a single health check or a comma-separated list of health checks. The default is 'all' checks.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
health_typeNoOptional. Valid values are: all, buffer, connection, constraint, index, replication, sequence, vacuum.all

Implementation Reference

  • The analyze_db_health tool implementation in src/postgres_mcp/server.py. It uses the DatabaseHealthTool class to perform the checks.
    @mcp.tool(
        description="Analyzes database health. Here are the available health checks:\n"
        "- index - checks for invalid, duplicate, and bloated indexes\n"
        "- connection - checks the number of connection and their utilization\n"
        "- vacuum - checks vacuum health for transaction id wraparound\n"
        "- sequence - checks sequences at risk of exceeding their maximum value\n"
        "- replication - checks replication health including lag and slots\n"
        "- buffer - checks for buffer cache hit rates for indexes and tables\n"
        "- constraint - checks for invalid constraints\n"
        "- all - runs all checks\n"
        "You can optionally specify a single health check or a comma-separated list of health checks. The default is 'all' checks."
    )
    async def analyze_db_health(
        health_type: str = Field(
            description=f"Optional. Valid values are: {', '.join(sorted([t.value for t in HealthType]))}.",
            default="all",
        ),
    ) -> ResponseType:
        """Analyze database health for specified components.
    
        Args:
            health_type: Comma-separated list of health check types to perform.
                        Valid values: index, connection, vacuum, sequence, replication, buffer, constraint, all
        """
        health_tool = DatabaseHealthTool(await get_sql_driver())
        result = await health_tool.health(health_type=health_type)
        return format_text_response(result)
Behavior4/5

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

No annotations provided, so description carries full burden. It excellently discloses what each check evaluates (e.g., 'checks for invalid, duplicate, and bloated indexes', 'checks vacuum health for transaction id wraparound'). However, it doesn't explicitly state the tool is read-only/safe or describe the return format.

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?

Well-structured with an initial summary, detailed bullet list for check types, and parameter guidance. The length is appropriate for the complexity of 8 distinct check types. No redundant sentences, though the comma-separated note could be more concise.

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?

Comprehensive coverage of the functional domain (what health checks are performed). Without annotations or output schema, it successfully explains the operational scope, though it could benefit from a note about the output format or execution impact.

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?

Schema coverage is 100%, but the description adds significant semantic value by explaining what each enum value represents (e.g., what 'sequence' or 'buffer' checks mean in detail) and notes that comma-separated lists are accepted—context not explicit in the schema's type definition.

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?

Clear verb 'Analyzes' and resource 'database health'. The bulleted list of specific infrastructure checks (vacuum, replication, sequence, etc.) implicitly distinguishes this from sibling 'analyze_query_indexes' which focuses on query optimization. However, it doesn't explicitly contrast with siblings.

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?

Provides usage guidance for the parameter ('optionally specify a single health check or a comma-separated list'), but fails to specify when to use this tool versus alternatives like 'analyze_query_indexes' or 'execute_sql'. No prerequisites or exclusion criteria 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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