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mittalpk

mcp-server-pgvector

by mittalpk

describe_vector_table

Inspect a vector table's structure by retrieving its columns, indexes, and approximate row count for schema analysis and query optimization.

Instructions

Get columns, indexes, and an approximate row count for a table.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tableYes
schemaNopublic

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations, the description carries the full burden. It does disclose that the row count is 'approximate,' which is a useful behavioral detail. However, it does not explicitly state non-destructive nature or any requirements, though 'Get' implies read-only. Some behavioral context is present but incomplete.

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 of 11 words, front-loaded with the action and object. Every word earns its place with no fluff or repetition.

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?

For a simple metadata-description tool with an output schema available, the description adequately covers what the tool returns and hints at the approximate nature of the row count. It lacks explicit parameter clarifications, but the overall simplicity and presence of output schema make it nearly complete.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate for lack of parameter documentation. It does not explain the 'schema' parameter or its default behavior, and only indirectly references 'table.' The parameter names are self-explanatory, but the description adds no meaning beyond the raw schema fields.

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 uses a specific verb ('Get') and clearly identifies the resource ('columns, indexes, and an approximate row count') and target ('a table'). It is immediately distinguishable from sibling tools such as list_vector_tables and hybrid_search, which serve different purposes.

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?

The description implies usage when one needs table metadata, but it does not explicitly state when to use this tool versus alternatives. No exclusions or alternative tool references are provided, so guidance is only implicit.

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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