Get an ETF's sector allocation (sector weights, heaviest first). When the ticker is not an ETF, is_etf is false and sectors is empty; is_etf: true with zero sectors means a real ETF whose sector data isn't ingested yet.
| Name | Required | Description | Default |
|---|---|---|---|
| ticker | Yes | ETF symbol. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses key behavioral traits: non-ETFs return `is_etf: false` with empty `sectors`, and `is_etf: true` with zero sectors indicates missing ingested data. This adds valuable edge-case context beyond mere retrieval.
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 two sentences: first states the core action, second explains edge cases. It is front-loaded with the essential purpose and contains no redundant or filler content. 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?
Given there is no output schema, this simple one-parameter tool describes return behavior sufficiently for an agent to interpret results correctly. It mentions the `is_etf` and `sectors` fields and covers edge cases. It could be more explicit about the overall response structure, but it is complete for 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?
The schema already provides 100% coverage for the single parameter `ticker` with description 'ETF symbol.' The tool description adds no additional parameter semantics beyond what the schema states, so the baseline score of 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 'Get an ETF's sector allocation' with specific verb and resource, and further clarifies 'sector weights, heaviest first.' This distinguishes it from sibling tools like get_ticker_holdings or get_ticker_bars. The purpose is unambiguous and specific.
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 that this tool is for ETF sector allocation and explains what to expect for non-ETFs, but it does not explicitly state when to use this tool versus alternatives like get_ticker_holdings. The guidance is more about interpreting results than choosing between tools, so it falls short of explicit usage exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools target distinct resource-action pairs, but get_ticker and get_ticker_history overlap (both return a single historical row via asof), and the four subscribe_* tools are thin wrappers around create_webhook, creating some ambiguity. Detailed descriptions mitigate but don't eliminate confusion.
The tickerbot_ prefix and verb_noun pattern are consistent (create_*, get_*, list_*, delete_*, update_*), but subscribe_* deviates from create_* for webhooks, and scan/search_news are bare verbs. Overall predictable.
35 tools is excessive for the domain; many could be consolidated (e.g., four subscribe_* variants into a single parameterized webhook creator, get_ticker/get_ticker_history into one). The breadth of data types justifies some volume, but this exceeds the 25-tool threshold for coherence.
Full lifecycle coverage for universes, custom signals, and webhooks; extensive read-side for tickers, series, bars, events, news, and scans. No critical dead ends: anything creatable can be updated/deleted/tested, and data lookups have appropriate query tools.
