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Vi Assistant by Vultax

by io.github.Vultax
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get_research_dataset

Read-onlyIdempotent

Retrieve published study datasets and CSV/JSON links with units, observation periods and limitations.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "slug": "<slug>"
    },
    "name": "get_research_dataset"
  }
}

Input Schema

NameRequiredDescriptionDefault
slugYes
datasetNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3/5.0
Behavior3/5

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

Annotations already declare readOnly=true, idempotent=true, non-destructive, closed-world, so the safety profile is covered. The description adds that the resource contains limitations and observation periods, which is useful downstream context. It doesn't disclose pagination, error behavior for an unknown slug, or the dataset parameter's effect, so it does only modest value-add 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?

Single sentence, front-loaded with verb and resource, then the payload. No waste; a little terse for a two-parameter lookup, but efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description's brief list of return contents (links, units, periods, limitations) is a helpful substitute. But with 0% parameter docs and no usage context, the agent lacks enough to know how to pick correct slug/dataset values.

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% on 2 params. The description never explains 'slug' (study identifier, lowercased pattern) or 'dataset' (which dataset within a study, optional). The agent must infer the relationship between the two params from names alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ('Retrieve') and resource ('published study datasets') with the payload ('CSV/JSON links with units, observation periods and limitations'). Clear enough to distinguish from siblings like get_research_article, though it doesn't name them explicitly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No when-to-use, when-not-to-use, or alternative guidance. The agent must infer it belongs to a research-data flow versus get_research_article or search_research on its own.

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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