tw-market-data

Replay a cited query

memory_replay_query

Read-onlyIdempotent

Re-run a remembered query by its twmd_q_… id, through the read API's own replay store.

    This is what makes a recalled finding checkable: the memory says where it came from, and
    this fetches that same data again. It never re-executes the query by another route — two
    implementations of "replay" would be two answers to a question whose whole value is having
    one.

    `status` is one of:
        found       the bytes are here, with `result_hash` to check them against
        too_large   it WAS served, but exceeded the size ceiling: `result` is absent and
                    `result_hash` is authoritative — you can still verify a copy you hold
        not_found   never recorded (or pruned) — the citation cannot be resolved

    Args: query_id — the `twmd_q_…` reference carried on a remembered finding's citation.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`query_id`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`hint`	No
`as_of`	No
`error`	No
`params`	No
`reason`	No
`result`	No
`status`	No
`blocked`	No
`dataset`	No
`query_id`	No
`available`	No
`namespace`	No
`result_hash`	No
`result_bytes`	No
`schema_version`	No
`result_row_count`	No
`not_investment_advice`	No

TDQS

A4.7/5.0

Behavior5/5

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

Annotations already convey read-only, open-world, idempotent, and non-destructive behavior, and the description richly supplements them: it defines the three status outcomes, explains when result is absent, identifies result_hash as authoritative in too_large cases, and clarifies what not_found means. This is exactly the behavioral detail an agent needs beyond annotations.

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 front-loaded with the core action and then organized into a compact status list. The philosophical aside about two implementations of 'replay' adds useful rationale but is slightly less essential than the rest.

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 only one parameter, a rich output schema, and strong annotations, the description fully covers invocation semantics, status outcomes, parameter origin, and behavioral guarantees. Nothing an agent needs to call this safely and interpret results is missing.

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?

The schema provides only a bare string field, so the description carries full weight. It explains the twmd_q_… format and specifies that the id comes from a remembered finding's citation, giving the agent everything needed to source and pass the parameter correctly.

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?

States a specific verb-resource pair: re-run a remembered query by its twmd_q_… id through the replay store. It clearly differentiates from a generic replay concept by noting it uses the read API's own replay store and never re-executes via another route, so it stands apart from siblings like replay_backtest.

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 gives clear context: use this to fetch the data behind a remembered finding's citation so the finding is checkable. It also communicates an important boundary—never re-executes the query by another route—but it does not explicitly name sibling tools or state when-not-to-use conditions in a direct comparison.

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.

Resources

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