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Export Research Dataset

export_research_dataset

Read-onlyIdempotent

Export knowledge-graph entities into JSON for downstream analysis. Load variants, proteins, or diseases into pandas, R, or bioinformatics pipelines to enable ML feature engineering and statistical modeling.

Instructions

Export the stored knowledge-graph 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

Name	Required	Description	Default
`params`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv1.4.3Jul 30, 2026
- addedInput schema / properties / params / properties / limit_per_table / description
```
Added value: +"Maximum rows to export per table (1–50000)."
```
First observedv1.1.8May 29, 2026

TDQS

A3.9/5.0

Behavior3/5

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

Annotations already declare readOnlyHint and idempotentHint. The description adds return format and example usage, but does not disclose pagination, error handling, or auth requirements. Adds modest value 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 concise with a clear front-loaded purpose statement, bullet lists, and a helpful code example. Slightly longer due to example but earns its place.

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 tool's complexity (nested params, output schema exists), the description covers purpose, use cases, and provides an example. It does not need to detail return values since output schema exists. Adequately complete.

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 already provides descriptions for both parameters (tables and limit_per_table). The description includes an example using the 'tables' parameter but adds no new semantic meaning. Baseline 3 applies as schema coverage is sufficient.

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 knowledge-graph data for downstream analysis, with specific verb 'Export' and resource 'knowledge-graph data'. It distinguishes from sibling lookup/query tools by focusing on bulk export.

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 provides explicit use cases (loading into pandas, R, etc.) implying when to use. However, it does not explicitly state when not to use or name alternative tools, though sibling tools are distinct.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.