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MCPg - Production-grade PostgreSQL MCP Server

Describe table

describe_table
Read-only

Describe the columns of a PostgreSQL table, returning name, data type, nullable, default, and vector dimension for each column in ordinal order.

Instructions

Describe the columns of a table, in ordinal order. Returns a list of objects with name, data_type, nullable, default, and vector_dimension (set only for pgvector vector(N) columns).

Example: describe_table(schema='public', table='users')

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
resultYes
Behavior4/5

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

Annotations already declare readOnlyHint=true, and the description adds return field details (name, data_type, nullable, default, vector_dimension) and an example call. This clarifies behavior beyond the annotations, though no additional side effects or auth needs are mentioned.

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 (two sentences plus an example), front-loaded with the core action, and every sentence serves a purpose. No extraneous information.

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?

Given the presence of an output schema, the description adequately lists key return fields and provides an example. It covers the main use case but omits mention of the optional database parameter's role (though the schema covers it). Suitable for the tool's simplicity.

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 low (33%), with only the database parameter described. The description provides an example with schema and table parameters but does not explain their semantics individually. It adds some value but not comprehensive compensation for the 67% undocumented parameters.

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 columns of a table in ordinal order, with specific verb 'Describe' and resource 'columns of a table'. It distinguishes from sibling tools like list_tables (which lists tables) and list_schemas (which lists schemas).

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 explicit guidance on when to use this tool versus alternatives. While the example shows usage, there is no mention of when to prefer describe_table over other schema-related siblings like list_tables or summarize_table. The description lacks selection criteria.

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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