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MCPg - Production-grade PostgreSQL MCP Server

Recommend Postgres conf

recommend_postgres_conf
Read-only

Compute PostgreSQL configuration recommendations based on system resources and workload type, without accessing any database.

Instructions

Compute pgtune-style postgresql.conf recommendations. Pure calculator — touches no database. Given total_ram_mb (required), cpu_count (default 4), workload (one of web/oltp/dw/desktop/mixed, default mixed), storage (one of ssd/hdd/san, default ssd), and an optional max_connections override, returns recommended values for shared_buffers, effective_cache_size, work_mem, maintenance_work_mem, wal_buffers, min_wal_size/max_wal_size, checkpoint_completion_target, default_statistics_target, random_page_cost, effective_io_concurrency, and the parallel-worker knobs. Memory fields are postgres-ready strings; settings is the same data as a flat {guc: value} dict for direct rendering. Pair with audit_settings (audit first, then size).

Example: recommend_postgres_conf(total_ram_mb=16384, cpu_count=8, workload='oltp', storage='ssd')

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
storageNossd
workloadNomixed
cpu_countNo
total_ram_mbYes
max_connectionsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Annotations set readOnlyHint=true and openWorldHint=false, and the description reinforces that it is a pure calculator with no database side effects. It also lists all output fields, providing full transparency without contradicting annotations.

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?

The description is informative and front-loaded with the main purpose, but the list of output fields is somewhat dense. The example adds clarity. Overall well-structured, though slightly verbose.

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

Completeness5/5

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

Given the tool's complexity, the description covers purpose, parameters, output, usage context (pairing), and provides an example. It is complete for an agent to understand and invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Despite 0% schema description coverage, the description thoroughly explains each parameter: total_ram_mb is required, cpu_count defaults to 4, workload enum values, storage enum values, and optional max_connections override. This adds significant meaning beyond the schema's default values.

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 that the tool computes pgtune-style postgresql.conf recommendations, specifying it's a pure calculator that touches no database. It distinguishes itself from siblings by its calculator nature and mentions pairing with audit_settings.

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

Usage Guidelines4/5

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

The description explains that it's a read-only calculator safe to call anytime, and explicitly advises pairing with audit_settings (audit first, then size). It does not list when not to use it or alternatives, but the context is clear enough for usage guidance.

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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