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MCPg - Production-grade PostgreSQL MCP Server

Lint naming conventions

lint_naming_conventions
Read-only

Detect naming convention violations in a Postgres schema: flags table, column, and index names that deviate from the schema's majority style or missing conventional prefixes.

Instructions

Lint table / column / index naming in a schema. Detects the majority case style (snake_case / camelCase / PascalCase / SCREAMING_SNAKE) per schema and per table, then flags outliers. Also flags indexes whose names do not start with a conventional prefix (idx_, ix_, pk_, uq_, fk_ by default). Findings carry the offender's style and the detected majority — agents can use the style field to filter for renames vs accept-as-is. Pure read. Returns an object with schema_style (detected majority), findings (list of style outliers), and index_prefix_findings (indexes with non-conventional prefixes).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
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
schemaYes
findingsYes
schema_majority_styleYes
Behavior3/5

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

Annotations already declare readOnlyHint=true; the description echoes 'Pure read' which adds no new behavioral insight. It does add useful context about detection logic and return fields, which provides moderate extra transparency beyond the annotations.

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 well-structured paragraph that front-loads the main purpose and then explains detection details and return structure. Every sentence adds value; no redundant or filler content.

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 output schema exists, the description adequately covers what the tool detects and returns. It might be missing limits on findings or performance notes, but for a naming lint tool it is sufficiently complete.

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?

The description adds meaning to the required 'schema' parameter (which lacks a description in the schema) by stating the tool lints naming in a schema. For the 'database' parameter, the schema already provides a thorough description. Overall, the description compensates for the 50% 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 uses a specific verb ('Lint') and clearly identifies the resource ('table / column / index naming in a schema'). It distinguishes itself from sibling tools like list_indexes or list_tables by focusing on naming convention analysis, not mere listing.

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 when to use (to check naming consistency) but does not explicitly state when not to use or mention alternative tools. No exclusion criteria or sibling comparisons are provided, so the guidance is merely implied.

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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