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MCPg - Production-grade PostgreSQL MCP Server

Describe table

describe_table
Read-only

Get column details for a PostgreSQL table, including name, data type, nullability, default, and vector dimension. Set fresh=true to re-read live after schema changes.

Instructions

Describe the columns of a table, in ordinal order. Set fresh=true to bypass the cache and re-read live (e.g. after a schema change). 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
freshNo
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
Behavior5/5

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

Adds valuable behavioral context beyond annotations: mentions caching by default, ability to bypass cache with `fresh=true`, and specifies return fields. No contradiction with readOnlyHint 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?

Two concise sentences plus an example, front-loaded with the main action. 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 simplicity, presence of annotations, and existence of an output schema, the description fully covers essential context: caching behavior, return format, and typical usage via example.

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?

With only 25% schema description coverage, the description compensates by explaining the `fresh` parameter and providing an example. The schema already describes `database`; `schema` and `table` are self-explanatory. Adds meaning beyond bare schema.

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?

Clearly states it describes columns of a table in ordinal order, distinguishing from siblings like list_tables or describe_self. The verb 'describe' + resource 'table' is specific and unambiguous.

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?

Provides an example but no explicit guidance on when to use vs alternatives or when not to use. The context implies usage for retrieving column metadata, but lacks comparative or exclusionary advice.

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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