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export_robvis

Combine risk-of-bias assessment records from multiple runs into a robvis-ready CSV, converting V2 judgements to V1 layout for accurate Cochrane-style figures.

Instructions

Combine assessment RECORDS from any number of runs into a CSV for robvis (McGuinness & Higgins), the standard tool for Cochrane-style risk-of-bias figures.

A review of 200 studies is 200 separate runs — each assessment costs a session, and nothing in this server survives between them. So pass records: the record object each submit_answers(domain=0) returns. They are small, flat and JSON-native, so a whole review's worth fits in one context, and they carry their own provenance so every row stays traceable to a document and an algorithm fingerprint. Omit records to use only what was assessed in THIS session (convenient, but session-scoped).

READ THE RETURNED losses BEFORE PUBLISHING THE FIGURE. It reports records that are not yet ratified, mixed C4 variants, equal weighting, and records built under differing algorithm transcriptions — each of which would make the figure claim more than the assessments support.

This is not a column dump, because robvis's ROBINS-I template is ROBINS-I V1 and V2 is not drop-in compatible:

  • V1 has SEVEN domains and orders selection of participants BEFORE classification of interventions. V2 has six and swaps that pair. Writing V2's columns out in order loses no data and raises no error — it just prints your classification judgement under the heading "Bias due to selection of participants". layout='robins_i' (the default) places each V2 judgement into its correct V1 SLOT and marks the dropped deviations domain NA. Upload it with tool='ROBINS-I'.

  • layout='generic' writes six columns headed with V2's own domain names, for tool='Generic'. The headings are then right, but robvis relabels the judgements into ROB1's vocabulary — Moderate becomes "Some concerns", Serious becomes "High". Prefer 'robins_i'.

Neither layout can carry 'Low, except for concerns about uncontrolled confounding': robvis reduces every cell to its first initial over a five-fill palette, so it collapses to Low whatever string is written. Say so in the figure caption.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
labelsNo
layoutNorobins_i
recordsNo
weightsNo
review_idNo
result_idsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
csvNo
rowsNo
usageNo
headerNo
layoutNo
lossesNo
summaryNo
filenameNo
n_resultsNo
review_idNo
robvis_toolNo
content_typeNo
slot_mappingNo
content_base64No
Behavior5/5

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

With no annotations, the description carries the full burden. It discloses session-scoped behavior, that `losses` reports unratified records, mixed C4 variants, equal weighting, and differing algorithm transcriptions. It also explains the V2-to-V1 slot mapping, the relabeling in generic layout, and the limitation with 'Low, except for concerns about uncontrolled confounding'. This is exceptional transparency.

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: front-loaded purpose, bolded warnings, and bulleted layout details. Each section earns its place given the complexity. It is not overly verbose for the amount of critical behavioral and layout information it conveys.

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?

The description thoroughly covers the core workflow (records, layout, losses) and important caveats. It lacks explanation of the remaining parameters, but overall it is highly complete for a complex tool with no annotations. An output schema exists, so return values are already specified elsewhere.

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

Parameters3/5

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

Schema coverage is 0%, so description must compensate. It provides deep, valuable semantics for `records` (the object from submit_answers) and `layout` (difference between robins_i and generic). However, it does not mention `labels`, `weights`, `review_id`, or `result_ids`, leaving four of six parameters undocumented. This is a partial but incomplete compensation.

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: 'Combine assessment RECORDS from any number of runs into a CSV for robvis'. It also names the exact output (robvis CSV) and distinguishes it from siblings by focusing on exporting assessment records for the risk-of-bias figure tool.

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?

The description explicitly explains when to pass `records` vs. omit it ('Omit records to use only what was assessed in THIS session'), when to read `losses` before publishing, and which layout to prefer ('Prefer 'robins_i' over 'generic'). It also clarifies when to use `layout='generic'` as an alternative.

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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