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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,

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.5/5.0
Behavior2/5

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

The description mentions the source system views but does not disclose potential performance impact, staleness of data, or any prerequisites. Since no annotations are provided, the description carries full responsibility for behavioral disclosure, which it fails to satisfy.

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 sentence that directly states the tool's purpose without any extra words. It is concise and front-loaded.

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 tool has no parameters, no output schema, and simple functionality, the description is minimally adequate. However, it could mention typical use cases or what the output contains (e.g., statistical counters) to be more complete.

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?

There are no parameters, so the schema already covers 100% of the interface. The description does not need to add parameter semantics, and it correctly omits any. Baseline score of 4 is appropriate.

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 identifies the tool as retrieving statistics about database activity and the background writer, naming the specific source views (pg_stat_database and pg_stat_bgwriter). This is distinct from sibling tools like get_database_connections which focus on connections.

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, nor any conditions or typical use cases. The description lacks contextual cues for an agent to decide if this is the appropriate tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.