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Tickerbot MCP Server

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by tickerbot

tickerbot_list_events

Query corporate events across tickers: dividends, splits, insider transactions, analyst actions, with filters for date, kind, firm, and more.

Instructions

THE events primitive — one timeline over every event log, cross-ticker: dividends, splits, insider transactions, and analyst actions ("all splits this month", "every analyst action in my universe this week", "AAPL's full corporate history" via ticker), plus two opt-in kinds that join only when named: signal (boolean-flag firings) and news. Rows are { ticker, ts, kind, payload }, newest first. Analyst payloads (history to 2012) carry firm/analyst/action/rating/price_target; filter them with firm=/action= (structured, case-insensitive — a q payload match is case-SENSITIVE) — actions: upgrades, downgrades, initiates_coverage_on, maintains, reiterates, assumes, reinstates, suspends, terminates_coverage_on. Use q for other payload conditions: it speaks the SQL grammar over exactly (ticker, ts, kind, payload jsonb); group_by/select/having roll the stream up (aggregates return truncated: true instead of paginating); join=state attaches the ticker's state as of each event. Requires at least one bound: a ticker scope (ticker/tickers/universe), a time window (from/to), or firm/action — q alone is not a bound.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoSQL WHERE over (ticker, ts, kind, payload jsonb) — ONLY those four identifiers. Payload fields via jsonb operators: payload->>'firm' = 'Goldman Sachs', (payload->>'shares')::numeric > 1e6. On kind=signal this requires `signal` (the firing log is ~175M rows); kind=signal takes no group_by.
toNoWindow end: a bare YYYY-MM-DD means through the END of that day (matching bars/series/spans); a timestamp is exclusive — events strictly before it. `until` accepted as alias.
dirNoAggregate-mode sort direction.
firmNokind=analyst only. Case-insensitive analyst-firm filter (e.g. "Goldman Sachs" matches "goldman sachs") — prefer this over a q payload match, which is case-sensitive.
fromNoEvents at or after this ISO date/datetime (inclusive; a bare YYYY-MM-DD means from the start of that day). `since` accepted as alias.
joinNojoin=state attaches each event's ticker STATE as of that event's moment (the replay join) under a `state` key — "downgrades where rsi_14 was already under 40" composes with q. Free on every plan.
kindNoComma list of kinds to include. Default is the four corporate kinds: dividend, split, insider, analyst. Two more are opt-in and join only when named: `signal` (boolean-flag firings) and `news`.
limitNoPage size. Max 1000.
orderNoAggregate-mode sort column/alias. Default: events.
actionNokind=analyst only. Exact rating-action filter.
cursorNoOpaque cursor from a prior response — carries the original filters, pass it alone (long q values must be resent alongside it).
havingNoPost-aggregation filter (requires group_by), e.g. COUNT(*) > 5.
selectNoAggregate-mode output columns (requires group_by). Default: group keys + COUNT(*) AS events.
signalNokind=signal only. One built-in boolean flag (e.g. golden_cross). REQUIRED to use `q` or `join` on kind=signal — naming the signal is what keeps the query on an index; optional otherwise.
tickerNoSingle-ticker filter, e.g. AAPL.
tickersNoComma-separated tickers, max 50. Mutually exclusive with `universe`.
group_byNoComma list of rollup keys — switches to aggregate rows, e.g. payload->>'firm' AS firm, or kind. `AS` names the JSON key; an un-named payload read is keyed by its payload key (payload->>'firm' -> firm).
intervalNoGrain for join=state replay (finest covering tier by default).
universeNoUniverse slug (top_10, top_100, or a saved one) to scope the stream. Mutually exclusive with `tickers`.
transitionNokind=signal only. `enter` (false->true) or `exit` (true->false). Always optional — an ordinary filter.
Behavior5/5

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

With no annotations, the description carries the full burden and does an excellent job. It discloses row format and ordering, analyst payload history, case-sensitivity nuances between structured filters and q, aggregate behavior (truncated: true instead of pagination), join semantics, and the mandatory bound condition. This is far beyond a minimal description.

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

Conciseness4/5

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

The description is long but information-dense, covering a complex 20-parameter tool. It is front-loaded with the core purpose and examples, then logically flows to row format, analyst specifics, q grammar, aggregation, join, and bounds. While every sentence earns its place, it could be tightened to improve scannability without losing critical details.

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?

Given the tool's complexity (20 parameters, no output schema, no annotations), the description is remarkably complete. It explains the return row shape, the difference between default and opt-in kinds, analyst filter behavior, q usage, aggregation response (truncated: true), join=state behavior, and the essential bound requirement. This covers the vast majority of what an agent needs to correctly invoke the tool.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3. The description adds meaningful high-level semantics: it explains that q 'speaks the SQL grammar over exactly (ticker, ts, kind, payload jsonb)', describes aggregation with group_by/select/having, and clarifies the bound requirement across multiple parameters. This goes beyond the per-parameter schema descriptions, though it does not dive into individual parameter syntax.

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 clearly states the tool's purpose: 'THE events primitive — one timeline over every event log, cross-ticker: dividends, splits, insider transactions, and analyst actions'. It gives concrete examples ('all splits this month', 'every analyst action in my universe this week') and distinguishes it from siblings like list_signal_events by noting signal and news are opt-in kinds. The verb+resource+scope is specific and unambiguous.

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

Usage Guidelines4/5

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

The description provides clear context for when to use the tool: as the events primitive covering all event logs, with optional kinds (signal, news) and aggregate/join modes. It also gives a critical constraint: 'Requires at least one bound: a ticker scope, a time window, or firm/action — q alone is not a bound.' However, it does not explicitly name alternative tools or state when to prefer them over this one, so it falls short of a 5.

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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