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

Annotations already establish readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds useful behavioral detail: findings are ordered worst first, grouped by risk class, written in Arabic with English labels, and linked to Arabic lessons.

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

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

The description is dense but front-loaded, first stating what is explained and then adding ordering, language, linking, and offline behavior. Every clause carries relevant information, though it could be slightly tighter.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-parameter, read-only explanation tool with no output schema, the description gives enough detail about what the findings contain, how they are ordered, what languages are used, and that it works offline. Nothing essential is missing for correct invocation.

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

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

Schema coverage is 100%, and the single result parameter is already documented as the JSON result cell from rls_check_query. The description reinforces that it operates on a pasted result, but adds no new syntax or format details beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb/resource: it explains the output of rls_check_query table by table. It also distinguishes the tool from the sibling query tool by emphasizing explanation of a pasted result, not execution of a check.

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

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

It clearly indicates the tool is used on the result from rls_check_query and that it works offline on the pasted result. It does not explicitly state exclusions or when not to use it, but the context is strong and unambiguous.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=false, and the description adds non-obvious behavior the annotations cannot express: this tool emits a query rather than executing it, touches zero rows, and returns a single JSON result cell that must be handed off. That is exactly the extra context the bar asks for when annotations exist.

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

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

Three sentences, each load-bearing: what it returns, what it does not touch, and how to run and hand off the result. The scope detail is front-loaded before the operational instructions.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description still characterizes the return value (a SQL query text, whose execution yields a single JSON cell) and the downstream consumer. Nothing an agent needs to call this correctly — where to run it, what comes back, where it goes next — is missing.

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

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

The input schema is an empty object with zero parameters, so there is nothing for the description to disambiguate and no risk of a caller mis-passing arguments. Baseline 4 applies; no parameter meaning is added or needed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and artifact ('Returns a read-only SQL query') plus the exact payload it enumerates: every table and view in the public schema with RLS state, anon/authenticated grants, columns, and policies. It is unambiguously distinct from its only sibling, which consumes the output rather than producing it.

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

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

Gives concrete execution contexts ('SQL Editor, or a Supabase MCP execute_sql tool') and the explicit next step ('pass the single JSON result cell to rls_check_explain'), which routes the agent through the sibling pair. It also pre-empts a likely misreading by stating it reads no row of data.

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