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smaniches
by smaniches

Export Research Dataset

export_research_dataset
Read-onlyIdempotent

Export accumulated research data as JSON-serializable dicts for downstream analysis in ML, R, Julia, or bioinformatics pipelines.

Instructions

Export accumulated research data for downstream analysis.

Returns all stored entities as JSON-serialisable dicts, suitable for:

  • Loading into pandas DataFrames for ML feature engineering

  • Importing into R or Julia for statistical analysis

  • Feeding into downstream bioinformatics pipelines

Example (Python)::

import pandas as pd
result = await export_research_dataset(ExportInput(tables=["variants"]))
df = pd.DataFrame(result["data"]["variants"])
high_tier = df[df["clinical_tier"] == "HIGH"]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

Annotations already indicate readOnly and idempotent behavior. The description adds context about output format but does not disclose any additional behavioral traits beyond what annotations convey.

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 concise with a clear structure: purpose, use cases, and a code example. Every sentence adds value without redundancy.

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 presence of an output schema, the description adequately covers purpose and usage. It lacks details on pagination or limits but addresses the key aspects for an export tool.

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% according to context signals, yet the description only includes a usage example for 'tables' and does not explain parameters or their constraints, failing to compensate for the lack of schema descriptions.

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 clearly states the tool exports research data for downstream analysis, specifying output as JSON-serialisable dicts. It distinguishes from sibling analysis tools by focusing on data export rather than analytical operations.

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 lists suitable downstream use cases (pandas, R, Julia, pipelines) and includes an example. However, it does not explicitly state when not to use it or provide alternatives among siblings.

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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