tw-market-data

Upcoming and past events

calendar

Read-onlyIdempotent

Sort corporate dates into what is still ahead and what has already passed.

★ TWO DATES, NOT ONE. What is "upcoming" is decided by the date the event HAPPENS; `as_of`
filters on the date it was ANNOUNCED. An ex-dividend declared on 2026-08-01 for 2026-09-15 is
both already known and still ahead on 2026-08-10. Collapsing the two fields either hides every
future date or reports last month's ex-dividend as though it were coming.

★ ELAPSED DATES ARE SEPARATED, NOT DISCARDED. They come back under `past` — the previous
ex-dividend is useful context for a question about the next one — but they can never appear
under `upcoming`.

★ NOT `search_filings` AND NOT `query_dataset`. Those retrieve disclosures and rows; this one
only arranges dated corporate events on a timeline relative to now.

⚠️ A scheduled date is a schedule, not a promise; companies move them.
Args: rows (dated events), today (YYYY-MM-DD), optional as_of.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`rows`	No
`as_of`	No
`today`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.7/5.0

Behavior5/5

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

Annotations declare readOnlyHint=true and idempotentHint=true, which already signal safety. The description adds crucial behavioral context: the two-date distinction (event vs announced), the separation of elapsed dates into 'past' rather than discarding them, and a warning that scheduled dates are movable. These are non-obvious traits not captured by annotations, enriching the agent's mental model.

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 well-structured with bullet points and a warning. It front-loads the core purpose and then explains nuances. Every sentence serves a purpose—clarifying the two-date logic, the past separation, and exclusions. Slightly verbose for a simple sort tool, but the complexity of the date logic justifies the length.

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

Completeness4/5

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

Given the output schema exists, the lack of return-value description is acceptable. The description covers when to use, how to interpret dates, and a caveat about schedule reliability. It doesn't mention what happens if rows is null, but the schema's default null implies it's handled. Overall, an agent would know exactly how to invoke this tool correctly.

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?

With schema description coverage at 0%, the description fully compensates for as_of and today: as_of filters on announcement date, today is the reference date in YYYY-MM-DD. rows is only called 'dated events', but given the tool's purpose and schema's additionalProperties:true, this is sufficient. It could name the field expected inside rows, but the context makes it clear.

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 opens with a clear, specific verb and resource: 'Sort corporate dates into what is still ahead and what has already passed.' It immediately distinguishes itself from sibling tools by naming search_filings and query_dataset as different retrieval tools. The purpose is unambiguous and distinguishable without opening schemas.

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 explicitly states when NOT to use this tool: 'NOT search_filings AND NOT query_dataset. Those retrieve disclosures and rows; this one only arranges dated corporate events on a timeline relative to now.' It also explains the nuanced as_of vs event date semantics, giving the agent a clear rule for when to call this tool and how to set parameters.

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

A4/5.0

Disambiguation4/5

The tools are largely distinct: querying, searching, backtesting, risk reads, alerts, memory, and audit functions each have clear homes. A few adjacent pairs (risk_read vs risk_assess, company_health_check vs positioning_read) could be confused, but the descriptions draw explicit boundaries.

Naming Consistency3/5

There are strong consistent clusters like list_*, get_*, run_*, and memory_*, but the *_read suffix alternates with noun-first names like company_health_check, and bare-verb tools like ask, calendar, chart, and screen break the pattern. The naming is readable but not uniform.

Tool Count2/5

Forty tools is well past the 25+ threshold and makes the surface heavy for an agent to navigate, even though the breadth reflects a genuinely wide platform. Several clusters could plausibly be consolidated without losing capability.

Completeness4/5

The tool surface covers the main lifecycle well: discovery, point-in-time querying, filings search and full text, backtesting, research, risk assessment, alerts, memory, approvals, and provenance verification. Minor gaps exist—no strategy management tools, no memory deletion, no bulk export—but agents can work around them.

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