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specify_result

Specify the one numerical result being assessed, with C4 accounts_for_deviations and B screening answers, to set the correct ROBINS-I question set and determine if domain scoring proceeds.

Instructions

Name the ONE numerical result being assessed, and settle C4. Required before any domain can be scored.

ROBINS-I assesses a single effect estimate, not a paper. A study reporting three outcomes across two analyses yields six assessments; give each its own result_id and run them separately.

accounts_for_deviations IS C4, and it is the highest-leverage input here: it swaps domain 1's entire question set and algorithm, so it cannot be deferred until domain 1 is reached. 'no_itt' — the analysis does NOT account for switches between the compared strategies or other protocol deviations. It targets the effect of assignment. Domain 1 variant A (baseline confounding only). 'yes_pp' — the analysis DOES account for them, by censoring, follow-up partitioning, or a g-method. It targets the effect of sustained receipt. Domain 1 variant B (baseline AND time-varying confounding). Judge this on what the ANALYSIS DOES, not on the label the authors give their estimand. A paper whose protocol table says 'observational analogue of the per-protocol effect' but which never censors at deviation is 'no_itt'. Getting this wrong means answering five questions that do not apply.

result_assessed is A1: the estimate with its precision. outcome is A3. result_location (A2) is where it appears and why it was chosen. target_trial is C1-C3 as a dict of labelled strings — eligible participants, intervention strategy, comparator strategy, and any note on the estimand. information_sources is D1: what you actually read. An NI answer is only defensible relative to what was searched.

b1/b2/b3 are the section B screening answers (Y/PY/PN/N). b1: was any attempt made to control confounding in this result? b2 (asked only if b1 is PN/N): is the potential for confounding great enough that an unadjusted result should not be considered further? b3: is the outcome measurement method unsuitable for the outcome it is meant to capture? Y/PY on b2 or b3 sends the result straight to critical and no domain is scored.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
b1No
b2No
b3No
modelNounspecified
outcomeYes
citationNo
documentNo
result_idYes
review_idNodefault
text_sha256Yes
target_trialNo
result_assessedYes
result_locationNo
information_sourcesNo
accounts_for_deviationsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
outcomeNo
citationNo
estimandNo
warningsNo
next_stepNo
result_idNo
review_idNo
screeningNo
text_sha256No
per_protocolNo
target_trialNo
domain1_variantNo
result_assessedNo
result_locationNo
domain1_questionsNo
information_sourcesNo
screening_terminatedNo
prespecified_confoundersNo
Behavior5/5

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

With no annotations, the description carries full weight and does so excellently. It discloses that accounts_for_deviations 'swaps domain 1's entire question set and algorithm', that getting it wrong 'means answering five questions that do not apply', and that b2/b3 Y/PY 'sends the result straight to critical'. These are meaningful behavioral consequences beyond simple operation.

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 long but dense and well-structured. It starts with a crisp purpose statement, then prioritizes the most important parameter (accounts_for_deviations) with examples and consequences, followed by concise definitions of remaining fields. Every sentence contributes workflow-critical information; 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?

Given the tool's complexity (ROBINS-I workflow), the description is remarkably complete. It covers purpose, prerequisite position, key decision logic, parameter semantics, and screening behavior (b1-b3). With an output schema present, the lack of return-value explanation is acceptable. The description enables correct invocation and understanding of downstream effects.

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 0%, so the description must compensate, and it does. It explains accounts_for_deviations with specific values ('no_itt'/'yes_pp') and its impact, maps result_assessed to A1, outcome to A3, result_location to A2, target_trial to C1-C3, information_sources to D1, and defines b1-b3 roles. This adds substantial meaning to most parameters, far exceeding the schema.

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 'Name the ONE numerical result being assessed, and settle C4', a specific verb+resource+action. It clearly states the tool's role as a required prerequisite ('Required before any domain can be scored'), distinguishing it from siblings like assess_result or submit_answers.

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?

It provides clear workflow context: this tool must be used before any domain scoring, and each result/analysis should be run separately via its own result_id. It also stresses that accounts_for_deviations cannot be deferred because it changes domain 1's question set. However, it does not explicitly name when-not-to-use alternatives, 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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