suggest_joins

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

The description adds valuable behavioral details beyond the readOnlyHint annotation: it scans up to 20 other datasets' PK candidates, uses a containment threshold of ≥95%, and is sample-based with 500 distinct values per source column. It also specifies that only non-PK source columns are considered. This gives the agent a clear model of the tool's operation and limitations.

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 compact and information-dense, with three sentences each adding meaningful context: the core action, the matching criteria, and the sampling methodology. No filler or redundant restatements of the tool name or schema are present.

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 tool with a single parameter, no output schema, and a read-only annotation, the description is remarkably complete. It covers the source dataset scope, the search process, the similarity threshold, the dataset limit, and the sampling behavior. It fully equips an agent to understand what the tool does and what to expect without needing additional documentation.

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 schema already fully describes the lone 'dataset' parameter as 'Source dataset identifier' (100% coverage), so the description adds only marginal value by reinforcing that it is the source dataset. The baseline of 3 applies because the schema carries the semantic weight; the description does not introduce additional parameter details or format expectations.

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's purpose with a specific verb ('Discover') and resource ('FK candidates'), and scopes the operation to a source dataset against other indexed datasets. It distinguishes itself from sibling tools like join_datasets (which likely executes joins) and suggest_keys (which suggest keys within a dataset).

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 clear context for when to use the tool: detecting foreign key candidates from a source dataset to other indexed datasets. It does not explicitly mention alternatives or when not to use it, but the scope and constraints (up to 20 datasets, containment threshold) effectively imply the appropriate use case.

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