Check Cross-Source Reconciliation

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

Annotations already signal readOnly, idempotent, open-world, and non-destructive behavior. The description adds valuable interpretive context beyond that: it warns that a discrepancy requires human review and does not prove either source is wrong, and the term 'published' indicates a precomputed snapshot. This is meaningful behavioral guidance not captured by annotations.

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?

Two sentences with no wasted words. The core action and return content are front-loaded, and the caveat about discrepancy interpretation is placed as a separate, purposeful sentence. Every sentence earns its place.

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 low-complexity, single-parameter read-only tool with full annotation coverage and an output schema, the description covers the essential behavioral and informational aspects. It explains what is returned and how to interpret deltas, leaving nothing critical missing.

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 coverage is 100%, with the single parameter dataset_name already documented with examples and a clear description. The tool description adds no additional parameter-level nuance, so the baseline of 3 applies.

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 states a specific verb ('Return') and a specific resource ('published cross-source reconciliation group'), and enumerates what is included (counts, dates, statuses, tolerances, contextual deltas). This clearly distinguishes it from sibling tools like get_freshness_summary or find_anomalies, which target different concerns.

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 implies its usage context: it returns an existing published reconciliation group rather than computing a fresh check. However, it does not explicitly state when to use this tool over alternatives, name any sibling, or exclude any condition (e.g., 'for live checks, use verify_dataset'). The guidance is adequate but not explicit.

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