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get_schema_recommendations

Get schema update recommendations for entity types by analyzing raw fragments, agent suggestions, or inference data to improve data structure.

Instructions

Get schema update recommendations for an entity type from raw_fragments analysis, agent suggestions, or inference.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entity_typeYesEntity type to get recommendations for
user_idNoUser ID for user-specific recommendations (optional)
sourceNoRecommendation source (default: all)
statusNoFilter by recommendation status (default: pending)
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states what the tool does but doesn't describe important behavioral aspects: whether this is a read-only operation, what format recommendations are returned in, whether there are rate limits or authentication requirements, or how recommendations are generated. The description is functional but lacks operational context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that clearly states the tool's purpose and scope. Every word earns its place with no redundancy or unnecessary elaboration. It's appropriately sized and front-loaded with the core functionality.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 4 parameters, no annotations, and no output schema, the description is insufficient. It explains what the tool does but doesn't address critical context: what format recommendations are returned in, whether this is a read operation, how recommendations are generated, or what happens when different sources are selected. The description should provide more operational context given the lack of structured metadata.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all four parameters thoroughly. The description mentions 'raw_fragments analysis, agent suggestions, or inference' which maps to the 'source' parameter's enum values, but adds minimal value beyond what the schema provides. The baseline 3 is appropriate when the schema does the heavy lifting.

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 the tool's purpose: 'Get schema update recommendations for an entity type' with specific sources (raw_fragments analysis, agent suggestions, or inference). It distinguishes from siblings like 'analyze_schema_candidates' by focusing on recommendations rather than analysis, but doesn't explicitly contrast with other schema-related tools like 'update_schema_incremental' or 'register_schema'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It mentions three sources for recommendations but doesn't explain when to choose one source over another or when to use this instead of sibling tools like 'analyze_schema_candidates' or 'update_schema_incremental'. No prerequisites or exclusions are mentioned.

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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