Skip to main content
Glama

lint_erd

Checks entire ERDs for structural integrity, referential integrity, duplicate names, identifier length limits, auto-increment misuse, and naming standard compliance in one pass.

Instructions

Lints the whole project — structure (PKs, attributes, descriptions, domains), referential integrity, duplicate physical names (including names that differ only by case, which collide on case-insensitive databases), duplicate logical entity names, identifier length limits (dialect-aware, tables/columns/indexes/FK constraints), auto-increment on non-integer column types (dialect-aware), and the naming standard (unregistered words, physical-name drift) in one pass. Quality/standards oriented, unlike validate_erd (structural validity).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourceYesTarget ERD — exactly one of a local file ({file}) or a server ERD ({erdId})
Behavior3/5

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

With no annotations provided, the description carries the full burden for behavioral disclosure. It thoroughly enumerates what the lint covers, but it does not explicitly state whether the operation is read-only, what output/return format it produces, or any side effects. Since 'lints' implies analysis, some transparency is present, but the missing output/error behavior is a gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, information-dense sentence enumerating many lint categories, followed by a comparative phrase. Each item adds value regarding the tool's scope, but the density makes it slightly less scannable than ideal, though it remains concise without redundancy.

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?

The description covers the full scope of lint operations and differentiates from validate_erd, and the input schema fully covers parameter usage. However, with no output schema, the description does not mention the return format or report structure, leaving a gap for agents that need to interpret results. Overall, it is adequate for selection and invocation.

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?

The input schema fully documents the single 'source' parameter with an anyOf structure for a local file or server ERD, achieving 100% coverage. The description adds no parameter-specific information, but this is unnecessary given the schema's clarity, so the baseline of 3 applies.

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 specifies the tool's purpose with a specific verb ('Lints') and resource ('the whole project'), followed by a detailed list of check categories (structure, referential integrity, duplicates, identifier length limits, auto-increment, naming standard). It distinguishes itself from validate_erd by contrasting quality/standards orientation with structural validity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states 'Quality/standards oriented, unlike validate_erd (structural validity)', which names an alternative and clarifies when to use this tool (for quality/standards checks) versus validate_erd (structural validity). This provides clear usage guidance, though it does not mention other overlapping tools like check_naming.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/sqemo/sqemo-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server