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abushadab

Self-Hosted Supabase MCP Server

by abushadab

get_database_stats

Retrieve database activity and background writer statistics from pg_stat_database and pg_stat_bgwriter for self-hosted Supabase instances, enabling developers to monitor and optimize database performance.

Instructions

Retrieves statistics about database activity and the background writer from pg_stat_database and pg_stat_bgwriter.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The main handler function for the 'get_database_stats' tool. It executes SQL queries against pg_stat_database and pg_stat_bgwriter to fetch database activity statistics and background writer stats, processes the results using helper functions, and returns them structured according to the output schema.
    execute: async (input: GetDbStatsInput, context: ToolContext) => {
        const client = context.selfhostedClient;
    
        // Combine queries for efficiency if possible, but RPC might handle separate calls better.
        // Using two separate calls for clarity.
    
        const getDbStatsSql = `
            SELECT
                datname,
                numbackends,
                xact_commit::text,
                xact_rollback::text,
                blks_read::text,
                blks_hit::text,
                tup_returned::text,
                tup_fetched::text,
                tup_inserted::text,
                tup_updated::text,
                tup_deleted::text,
                conflicts::text,
                temp_files::text,
                temp_bytes::text,
                deadlocks::text,
                checksum_failures::text,
                checksum_last_failure::text,
                blk_read_time,
                blk_write_time,
                stats_reset::text
            FROM pg_stat_database
        `;
    
        const getBgWriterStatsSql = `
            SELECT
                checkpoints_timed::text,
                checkpoints_req::text,
                checkpoint_write_time,
                checkpoint_sync_time,
                buffers_checkpoint::text,
                buffers_clean::text,
                maxwritten_clean::text,
                buffers_backend::text,
                buffers_backend_fsync::text,
                buffers_alloc::text,
                stats_reset::text
            FROM pg_stat_bgwriter
        `;
    
        // Execute both queries
        const [dbStatsResult, bgWriterStatsResult] = await Promise.all([
            executeSqlWithFallback(client, getDbStatsSql, true),
            executeSqlWithFallback(client, getBgWriterStatsSql, true),
        ]);
    
        // Use handleSqlResponse for each part; it throws on error.
        const dbStats = handleSqlResponse(dbStatsResult, GetDbStatsOutputSchema.shape.database_stats);
        const bgWriterStats = handleSqlResponse(bgWriterStatsResult, GetDbStatsOutputSchema.shape.bgwriter_stats);
    
        // Combine results into the final schema
        return {
            database_stats: dbStats,
            bgwriter_stats: bgWriterStats,
        };
    },
  • Zod schemas defining the input (empty), output structure for database and bgwriter stats, and static MCP input JSON schema for the tool.
    // Schema for combined stats output
    // Note: Types are often bigint from pg_stat, returned as string by JSON/RPC.
    // Casting to numeric/float in SQL or parsing carefully later might be needed for calculations.
    const GetDbStatsOutputSchema = z.object({
        database_stats: z.array(z.object({
            datname: z.string().nullable(),
            numbackends: z.number().nullable(),
            xact_commit: z.string().nullable(), // bigint as string
            xact_rollback: z.string().nullable(), // bigint as string
            blks_read: z.string().nullable(), // bigint as string
            blks_hit: z.string().nullable(), // bigint as string
            tup_returned: z.string().nullable(), // bigint as string
            tup_fetched: z.string().nullable(), // bigint as string
            tup_inserted: z.string().nullable(), // bigint as string
            tup_updated: z.string().nullable(), // bigint as string
            tup_deleted: z.string().nullable(), // bigint as string
            conflicts: z.string().nullable(), // bigint as string
            temp_files: z.string().nullable(), // bigint as string
            temp_bytes: z.string().nullable(), // bigint as string
            deadlocks: z.string().nullable(), // bigint as string
            checksum_failures: z.string().nullable(), // bigint as string
            checksum_last_failure: z.string().nullable(), // timestamp as string
            blk_read_time: z.number().nullable(), // double precision
            blk_write_time: z.number().nullable(), // double precision
            stats_reset: z.string().nullable(), // timestamp as string
        })).describe("Statistics per database from pg_stat_database"),
        bgwriter_stats: z.array(z.object({ // Usually a single row
            checkpoints_timed: z.string().nullable(),
            checkpoints_req: z.string().nullable(),
            checkpoint_write_time: z.number().nullable(),
            checkpoint_sync_time: z.number().nullable(),
            buffers_checkpoint: z.string().nullable(),
            buffers_clean: z.string().nullable(),
            maxwritten_clean: z.string().nullable(),
            buffers_backend: z.string().nullable(),
            buffers_backend_fsync: z.string().nullable(),
            buffers_alloc: z.string().nullable(),
            stats_reset: z.string().nullable(),
        })).describe("Statistics from the background writer process from pg_stat_bgwriter"),
    });
    
    // Input schema (allow filtering by database later if needed)
    const GetDbStatsInputSchema = z.object({});
    type GetDbStatsInput = z.infer<typeof GetDbStatsInputSchema>;
    
    // Static JSON Schema for MCP capabilities
    const mcpInputSchema = {
        type: 'object',
        properties: {},
        required: [],
    };
  • src/index.ts:17-17 (registration)
    Import of the getDatabaseStatsTool definition into the main MCP server index file.
    import { getDatabaseStatsTool } from './tools/get_database_stats.js';
  • src/index.ts:106-106 (registration)
    Addition of the tool to the availableTools registry map, which is used to register tools with the MCP server.
    [getDatabaseStatsTool.name]: getDatabaseStatsTool as AppTool,
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. It states the tool retrieves statistics but does not describe the return format, potential rate limits, authentication requirements, or whether it's a read-only operation. This leaves significant gaps in understanding how the tool behaves beyond its basic purpose.

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 purpose without unnecessary words. It is front-loaded with the core action and resources, making it easy to understand at a glance.

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 for a tool that retrieves statistical data. It does not explain what statistics are returned, their format, or any behavioral traits like performance impact or data freshness. This leaves the agent with insufficient context to use the tool effectively beyond knowing its general purpose.

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?

The tool has zero parameters, and the schema description coverage is 100% (though empty). The description appropriately does not discuss parameters, as none exist, and instead focuses on what data is retrieved. This meets the baseline for a parameterless tool.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Retrieves statistics') and the exact resources involved ('database activity and the background writer from pg_stat_database and pg_stat_bgwriter'). It distinguishes itself from sibling tools like get_database_connections or list_tables by focusing on statistical metrics rather than connections or schema listings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for monitoring database performance metrics, but does not explicitly state when to use this tool versus alternatives like get_database_connections for connection stats or execute_sql for custom queries. No exclusions or prerequisites are mentioned, leaving usage context somewhat open-ended.

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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