fusion_describe_flexfields

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

The description discloses internal behavior beyond annotations: it reads fnd_df_segments_b/_tl, returns segment rows with COLUMN_NAME and CONTEXT_CODE, and clarifies that 'Context Data Element' rows describe the context chooser rather than data segments. It also gives practical semantics for SEGMENT_PROMPT and global vs. context-specific segments. Annotations already mark it read-only and idempotent, and nothing contradicts them.

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?

Though long, the description is densely informative with no filler. It front-loads the core question, provides a clear numbered protocol, includes concrete examples, and adds necessary caveats about context rows and prompt labels. Every sentence contributes operational value.

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 metadata-introspection tool, the description covers the full workflow: schema discovery, DFF code guessing, LIKE-pattern fallback, query construction, and context filtering. It explains return-relevant fields such as COLUMN_NAME, SEGMENT_CODE, CONTEXT_CODE, and SEGMENT_PROMPT, and the output schema removes the need to enumerate return structure. The limit parameter and its default are also addressed.

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?

Schema description coverage is 0%, so the description carries full responsibility for parameter meaning. It explicitly documents all three parameters: flexfield as a case-insensitive LIKE pattern with examples, context as an optional LIKE pattern over CONTEXT_CODE, and limit as a maximum row count. This fully compensates for the schema's lack of descriptions.

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 opening line states exactly what the tool does: maps DFF segments to the ATTRIBUTEn column that stores each one. It distinguishes itself from siblings like fusion_describe_table by explaining that this reads the DFF definition rather than the base table structure, and the protocol explicitly sequences the two.

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 'Protocol for an unknown table' section gives concrete when-to-use guidance: first describe the base table, then call this tool with the DFF code or a LIKE pattern, then project the returned column in an adhoc query. It also explains how to handle uncertain DFF codes and context-specific segments, which is exactly the kind of decision support an agent needs.

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