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tickerbot_list_signal_events

Occurrence SPANS of a boolean signal for one ticker, newest-first. For STATE flags (above_sma_50, in_uptrend) each row is a true-WINDOW: started_at when it flipped true, ended_at when it flipped back (null while still true), with prices at both ends. For EVENT flags (golden_cross, gap_up) each row is a point firing (started_at = ended_at). "Golden crosses in June" is from=2026-06-01&to=2026-06-30. Built-in booleans only — numerics and custom signals have no precomputed spans (use tickerbot_get_series).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNoWindow end (inclusive) on started_at; a bare YYYY-MM-DD means through the end of that day.
fromNoWindow start (inclusive) — YYYY-MM-DD, ISO, or epoch-ms. Overlap semantics: a span still open, or ended on/after from, is returned even if it started earlier.
limitNoPage size. Max 1000, newest first.
cursorNoOpaque cursor — pages older, inside the from/to window if one is set.
signalYesBuilt-in boolean flag name.
tickerYesSymbol.
merge_gap_secondsNoInterval-union: contiguous windows whose gap is ≤ N seconds collapse into one — de-fragments flags with thousands of per-tick rows (e.g. 3600 for daily flags). Default 0 = no merge. A cursor pins this; resend it unchanged when paging.

TDQS

A5/5.0
Behavior5/5

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

With no annotations provided, the description carries the full behavioral burden and does so thoroughly. It explains window semantics for state flags, null-ended_at behavior, point-firing semantics for event flags, overlap behavior for from/to, merge behavior, and paging direction, all beyond what the schema alone provides.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is packed with high-value information across a few sentences, front-loading the core purpose before diving into semantics. The example and exclusion are placed where they are most useful, and there is no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite 7 parameters, no output schema, and no annotations, the description covers what the tool returns, how state vs event rows differ, how date windows behave, how pagination works, how merge works, and when to use another tool. This is unusually complete for a list endpoint.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3, but the description adds substantial meaning: it ties from/to to a human-readable example, explains overlap semantics, clarifies default merge behavior with a concrete example, and notes that cursor pages older within the window. This goes well beyond the schema property descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb ('list'), resource ('occurrence spans of a boolean signal'), and scope ('for one ticker'), then distinguishes STATE flags from EVENT flags. It also explicitly excludes numerics and custom signals and points to tickerbot_get_series, making it clearly distinct from sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It provides an explicit when-to-use context ('Occurrence SPANS... for one ticker', 'built-in booleans only') and an explicit when-not-to-use alternative ('numerics and custom signals have no precomputed spans (use tickerbot_get_series)'). The 'Golden crosses in June' example also makes the intended query pattern concrete.

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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TDQS

A3.9/5.0
Disambiguation4/5

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.

Naming Consistency4/5

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.

Tool Count2/5

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.

Completeness5/5

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.