get_daily_picksToday's highest-scoring tickers as published on tapeline.io — the same rows an anonymous visitor sees, each with score, signal and the one-line reason.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | How many rows (1-10, default 10). |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must carry the behavioral weight. The phrase 'same rows an anonymous visitor sees' strongly implies a public, read-only list and reveals the return composition. It does not explain caching, rate limits, or timezone handling, but the description gives an adequate transparency baseline.
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?
One sentence with no wasted words: it states the resource, source, public behavior, and what a returned row contains. The key discriminator ('anonymous visitor sees') is front-loaded.
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, one-parameter command, the description is essentially complete. It names the response row contents, which matters because there is no output schema. It only stops short of stating a precise timezone or daily cutoff for 'today', which is a minor gap.
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 is already 100% covered for the single limit parameter, including its range and default. The description does not add any further parameter nuances, which is appropriate under the baseline-3 rule for high schema coverage.
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 identifies a specific verb-resource pair: retrieving today's top-scoring published tickers from tapeline.io. It also states the exact scope (the list an anonymous visitor sees) and the included row fields, which distinguishes it from per-ticker tools like get_ticker_record and get_ticker_score.
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 makes its use case clear: this returns the daily published top picks, not a specific ticker's record or score. It gives context about the public nature of the data, but it does not explicitly name alternatives or state when not to use this tool.
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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