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io.github.qso-graph/ionis-mcp

io.github.qso-graph/ionis-mcp

by qso-graph
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

List Datasets

list_datasets
Read-onlyIdempotent

Lists all available IONIS datasets with row counts, file sizes, and descriptions so you can identify what data is available for querying.

Instructions

Show available IONIS datasets and their statistics.

Lists all datasets found in the configured data directory with row counts, file sizes, and descriptions. Use this to see what data is available for querying.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_datasets"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.2.9

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is fully covered by structured data. The description adds that results come from the configured data directory, but the enumeration of returned fields duplicates the existing output schema rather than adding new behavioral context such as behavior on an empty or unconfigured directory.

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?

Front-loaded with the core action in the first sentence, followed by supporting detail. The closing 'Use this to see what data is available for querying' partially restates the opening, but overall there is little waste.

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?

For a zero-parameter, read-only listing tool with annotations carrying safety and idempotency and an output schema covering the return shape, the description covers everything an agent needs to decide and call. Only edge-case behavior (empty directory, missing configuration) is left unstated, which is minor given a defined output schema.

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

Parameters4/5

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

The tool takes zero parameters, so there is nothing for the description to disambiguate; baseline 4 applies. The description correctly does not invent options or filters that the schema does not support.

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 and resource ('Show available IONIS datasets and their statistics') and names the concrete returned fields (row counts, file sizes, descriptions), plus the data source ('configured data directory'). It is clearly distinguishable from analytical siblings like query_signatures or band_summary, though it never names a sibling explicitly.

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?

'Use this to see what data is available for querying' gives a clear discovery-first context in which this tool is the right call. It stops short of any exclusion or explicit alternative ('do not use when you already know the dataset name'), so it is strong context without routing rules.

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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