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rows_query
Read-onlyIdempotent

Rows matching filters ([{"column": "Stage", "op": "eq", "value": "New"}]; ops: eq, neq, contains, not_contains, gt, gte, lt, lte for numbers and dates, empty, not_empty, in), all or any of them, sorted by columns, a page at a time. Free.

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": "rows_query"
  }
}

Input Schema

NameRequiredDescriptionDefault
sortNo
limitNo
matchNoall
tableYesThe table's name (or its table_id).
offsetNo
filtersNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare the operation as read-only, idempotent, and non-destructive. The description adds useful behavioral context by noting pagination ('a page at a time') and cost ('Free'), which are not captured by the structured annotations.

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 dense sentence that front-loads the core purpose. Every clause adds information, though the inline example and operator list make it slightly harder to scan than ideal.

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 query tool with six parameters, no output schema, and sparse schema descriptions, the definition covers filtering, matching, sorting, pagination, and cost. It omits return shape details and explicit `limit`/`offset` semantics, but is largely sufficient for correct invocation.

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?

Schema description coverage is only 17%, so the description must compensate. It does so effectively for the complex `filters` parameter by listing operators and providing an example, and it clarifies `match` semantics ('all or any of them') and sorting/pagination, though it does not explicitly explain `limit` or `offset`.

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?

The description clearly states that the tool returns rows matching filters, sorted and paginated. It is distinct from siblings like rows_summarize or rows_add, though it does not explicitly name an alternative to differentiate against.

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?

Usage is implied by the filtering, sorting, and pagination behavior, but the description gives no explicit when-to-use guidance, prerequisites, or comparison to sibling tools such as rows_summarize or tables_list.

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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