sgraph_query

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

With no annotations provided, the description carries the disclosure burden. It clearly states the return type: 'Returns a filtered model (elements + associations), not tabular data' and specifies the JSON shape. It also documents case-sensitive and case-insensitive matching behaviors. It stops short of explicitly stating that the tool has no side effects, though 'query' and 'filter' imply a read-only operation.

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 long but every section earns its place: a query language reference requires syntax examples and operator explanations. The purpose and return type are front-loaded, and the syntax reference is clearly organized with headings. It is dense but not padded.

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 complex tool with no output schema and no annotations, the description is remarkably complete: it explains syntax, operators, examples, and return structure. It does not cover edge cases, error behavior, or how model_id interacts with the default model, but these are minor for selecting and invoking the tool correctly.

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 description adds substantial meaning to the expression parameter, offering a full syntax quick reference with element selection, attribute filters, dependency queries, logical operators, and examples. The optional model_id parameter is adequately explained in the schema itself, so the description's focus on the required expression parameter is appropriate.

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 opens with a specific verb and resource: 'Filter the model using SGraph Query Language' and lists concrete use cases: filtering sub-models, checking module dependencies, and attribute-based element selection. It also explicitly contrasts the tool with sgraph_cypher_query, making the distinction clear.

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?

The description says 'Best for' and explicitly directs users needing tabular queries and aggregation to sgraph_cypher_query instead. It also gives query-category examples that clarify when this tool is appropriate versus structural alternatives.

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