Filter and list stored ad swipe observations by vertical, offer, and minimum score to find relevant ad examples for your swipe file research.
List stored ad swipe observations filtered by vertical, offer, and minimum score.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of records to return. | |
| offer | No | Optional offer substring filter. | |
| vertical | No | Optional vertical substring filter. | |
| min_score | No | Minimum score from 0 to 100. |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While it implies a read operation, it doesn't explicitly state that it's read-only, doesn't mention pagination behavior, ordering, or how the filters interact. It doesn't contradict anything, but offers no insight into side effects or performance.
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?
A single, concise sentence that front-loads the main action and lists all filter dimensions. No filler or repetition.
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 simple list tool with no output schema and all parameters optional, the description is mostly adequate. However, given no annotations and no output schema, it could add info about default sorting or result count, but that's a minor gap given the tool's simplicity.
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?
With 100% schema description coverage, the schema already documents each parameter's purpose. The description adds a high-level summary of filtering but doesn't clarify filter semantics (e.g., substring matching, whether min_score is inclusive) beyond what the schema provides. Baseline 3 is appropriate.
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 a specific verb ('List'), resource ('ad swipe observations'), and the filtering dimensions (vertical, offer, minimum score). It distinguishes from siblings like record_ad_swipe and score_ad_pattern by focusing on retrieval, but doesn't explicitly name a sibling it is not.
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 usage through its filter parameters, but provides no explicit guidance on when to use this tool versus alternatives like summarize_winning_patterns or generate_servicehawk_variants. It doesn't state when not to use it or offer alternative tool suggestions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.