dataverse_validate_fetchxml

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

Beyond the annotations (readOnlyHint=true, idempotentHint=true, destructiveHint=false), the description adds critical behavioral traits: HTTP 200 does not imply validity, severity mapping is observed not documented, unknown severities are treated as errors, raw_response is returned unchanged, and the local XML parser rejects DTDs/entities. This exceeds what annotations convey.

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 lengthy but front-loaded with a crisp one-sentence purpose, followed by logically grouped paragraphs covering behavior, output shape, and edge cases. Every paragraph serves a distinct function, though some details (e.g., severity mapping) could be condensed without losing meaning. Overall it is well-structured and information-dense.

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?

Despite having an output schema, the description generously explains the response shape: count, error_count, warning_count, has_errors, errors, raw_response, and the normalized=false fallback. It also covers edge cases like HTTP 414 and non-200 validation errors. For a tool with this complexity and safety-critical validation semantics, the description is fully complete.

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 includes exhaustive descriptions for both fetch_xml and dataverse_url, including encoding constraints and shortening advice. The tool description adds a small contextual note about the root <entity name="..."> identifying the table, but this is also present in the schema. Since schema description coverage effectively handles parameter semantics, the tool description provides marginal added value, matching the baseline 3.

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 'Check a FetchXML query for problems and performance warnings WITHOUT running it', clearly identifying the verb (check), resource (FetchXML query), and distinguishing it from executing the query. It explicitly names the sibling dataverse_execute_fetchxml as the companion tool, making the tool's unique role unambiguous.

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 gives explicit usage guidance: 'Run it before executing an expensive or machine-generated query' and names dataverse_execute_fetchxml as the alternative when execution is desired. However, it does not mention when not to use it (e.g., for View XML) or list alternative validators like dataverse_validate_view, so it lacks explicit exclusions but still provides clear context.

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