tw-market-data

Replay a backtest

replay_backtest

Read-onlyIdempotent

Re-run a stored backtest and report whether it still produces the same numbers.

    Same spec, same `as_of`, same data questions. If the numbers moved, either the engine
    version changed or the underlying data was restated — both are reported, neither is
    smoothed over. Args: run_id.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`run_id`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`error`	No
`reason`	No
`run_id`	No
`blocked`	No
`reproduced`	No
`differences`	No
`not_investment_advice`	No
`replay_engine_version`	No
`original_engine_version`	No

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already signal read-only, idempotent, and non-destructive behavior. The description adds meaningful behavioral context beyond that: it reports discrepancies, does not smooth over changes, and identifies possible causes (engine version or data restatement). This helps the agent set expectations about the tool's output philosophy.

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 compact and front-loaded with the core purpose. Every sentence contributes value, including the behavioral note about not smoothing over discrepancies and the argument hint.

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?

For a single-parameter tool with rich annotations and an output schema, the description covers the purpose, behavioral expectations, and relevant interpretation of results. There is no prerequisite or edge-case information that an agent would critically need here.

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

Parameters2/5

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

Schema description coverage is 0% and the description only states 'Args: run_id' without explaining what run_id identifies or any constraints. The main text implies run_id refers to a stored backtest, but the description does not compensate for the missing schema documentation.

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 uses a specific verb ('Re-run') and resource ('stored backtest') and states the exact purpose: report whether it still produces the same numbers. It also clarifies scope with 'Same spec, same as_of, same data questions,' which distinguishes it from running a new backtest or fetching current results.

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 clearly implies when to use this tool: to verify reproducibility of a stored backtest and detect drift in engine behavior or data. It does not explicitly name alternatives like run_backtest or get_backtest, but the context is clear enough that an agent would know this is for replaying an existing run.

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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