execute_query

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

Discloses read-only nature, statement acceptance criteria, return format (JSON table with columns/rows/row_count/truncated), row cap defaults (10000 or SSAS_ROW_LIMIT), and truncation flag. With no annotations, the description fully covers behavioral traits.

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?

Concise, front-loaded with the primary action, uses bullet points and structured Args section. Every sentence adds value without redundancy.

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?

Given the presence of an output schema (context indicates yes), the description still adequately covers return format and row limiting. It also addresses accepted query types, making the tool fully understandable.

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?

Despite 0% schema coverage, the description fully explains both parameters: query is the MDX/DAX text, max_rows is an optional row cap (0 = server default). This adds complete semantic meaning beyond the bare 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 clearly states the tool executes read-only MDX or DAX queries against an SSAS database, with specific syntax for each. It differentiates from sibling tools (execute_dmv, list_metadata) by specifying the query types and database context.

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?

Explicitly guides when to use MDX vs DAX and lists accepted statement types (SELECT/WITH for MDX; EVALUATE/DEFINE for DAX). Implicitly excludes DMV queries and metadata calls, providing clear context for selection.

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