supabase-mcp-server

Select rows

select_rows

Read-only

Reads rows from a table using structured filters, ordering, and a limit of up to 100 while applying RLS for the anon role and avoiding raw SQL.

Instructions

Reads rows from one table with structured filters (eq, neq, gt, gte, lt, lte, like, ilike, in, is_null), ordering and a limit of at most 100. Rows are filtered by RLS for role anon. No raw SQL.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "table": "<table>"
    },
    "name": "select_rows"
  }
}

Input Schema

Name	Required	Default
`limit`	No
`table`	Yes
`offset`	No
`schema`	No	public
`columns`	No
`filters`	No
`orderBy`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Nov 1, 2026

TDQS

A3.8/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds genuinely useful behavior beyond that: rows are RLS-filtered for role anon (results may be silently incomplete), the limit is capped at 100, and raw SQL is rejected.

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?

Three short sentences, front-loaded with the core action, with the two global constraints (RLS, no SQL) appended. Zero filler.

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?

No output schema exists, so not explaining the return shape is acceptable, and the annotations cover safety. The main gaps are the undocumented offset/columns/schema parameters and no hint of pagination behavior, but the RLS disclosure is the critical piece and it is present.

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?

Schema description coverage is 0%, so the description must carry parameter meaning; it clarifies the filter operators and the 100-row cap. But it says nothing about offset, columns, schema namespace, or orderBy direction, leaving several of the seven parameters undocumented in both places.

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

Purpose4/5

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

States a specific verb and resource ('Reads rows from one table') and enumerates the operations supported (structured filters, ordering, limit). It implicitly distinguishes itself from siblings like count_rows and list_tables by describing row retrieval with filters, though it never names an alternative.

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?

'No raw SQL' and the filter operator list imply this is the way to do filtered reads rather than ad-hoc queries. However, there is no explicit guidance on when to use this versus count_rows or list_tables, and no mention of prerequisites such as knowing the table schema first (describe_table).

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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