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MCPg - Production-grade PostgreSQL MCP Server

Describe AO table

describe_ao_table
Read-only

Inspect append-optimized or columnar storage metadata for a table: orientation, compression type/level, block size, checksum. Returns false if table is heap or if the database lacks the feature.

Instructions

Describe append-optimized (AO) / append-optimized columnar (AO/CO) storage metadata for one table: row vs column orientation, compression_type, compression_level, block_size, checksum. Reads pg_appendonly. Returns is_ao=false cleanly when the table is a regular heap. Read-only. On vanilla PG returns available=false.

Example: describe_ao_table(schema='public', table='events_ao')

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tableYes
schemaYes
databaseNoOptional: target a configured secondary (read-only) database by name; omit for the primary. Call list_databases to see the configured ids.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
is_aoYes
tableYes
detailYes
schemaYes
checksumYes
columnarYes
block_sizeYes
compression_typeYes
compression_levelYes
Behavior4/5

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

The description explicitly says 'Read-only' which matches the readOnlyHint annotation. It also describes the clean return for non-AO tables and behavior on vanilla PG, providing behavioral context beyond the annotation.

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 concise with two sentences and one example. It front-loads the purpose and key details, with no wasted words.

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 and the presence of an output schema, the description covers all necessary aspects: metadata fields, edge cases (non-AO, vanilla PG), and read-only behavior. It provides a complete picture for the agent.

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 coverage is 33% with only the 'database' parameter having a description. The description includes an example with schema and table parameters but does not add details for the database parameter beyond what's in the schema. The example helps but does not fully compensate for the low schema coverage.

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 tool describes AO/AO/CO storage metadata for one table, listing specific attributes like compression_type, compression_level, etc. It distinguishes itself from sibling tools like describe_table by focusing on a specific table type.

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 specifies it is for one table, that it returns is_ao=false for regular heap tables, and mentions behavior on vanilla PG. While it doesn't explicitly state when not to use it or list alternatives, the niche is clear enough for an agent to select it appropriately.

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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