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opensanctions-mcp

opensanctions-mcp

by scka-de
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

get_dataset

Retrieve detailed information about sanctions datasets including description, publisher, entity count, last updated date, and coverage data. Use after identifying dataset names to access comprehensive compliance screening resources.

Instructions

Get full details about a specific dataset: description, publisher, entity count, last updated, and coverage information. No API key required.

Use list_datasets first to find dataset names, then this tool for details.

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": {
      "name": "<name>"
    },
    "name": "get_dataset"
  }
}

Input Schema

NameRequiredDescriptionDefault
nameYesDataset name (e.g. "us_ofac_sdn", "eu_fsf")

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It usefully adds context about 'No API key required' which is important operational information. However, it doesn't mention potential limitations like rate limits, error conditions, or response format details that would be helpful for a read operation.

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 perfectly structured with two focused sentences. The first sentence clearly states the purpose and scope, while the second provides essential usage guidance. Every word earns its place with zero wasted text or 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?

For a single-parameter read operation with no output schema, the description provides good context about what information is returned and how to use the tool. The main gap is the lack of information about the response format or structure, which would be helpful since there's no output schema. However, the description covers the essential operational aspects well.

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?

The input schema has 100% description coverage, with the single parameter 'name' well-documented in the schema. The description doesn't add any additional parameter information beyond what's already in the schema, so it meets the baseline of 3 where the schema does the heavy lifting.

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 specific action ('Get full details about a specific dataset') and lists the exact information returned (description, publisher, entity count, last updated, coverage information). It explicitly distinguishes this tool from its sibling 'list_datasets' by explaining their complementary roles.

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 provides explicit guidance on when to use this tool versus alternatives: 'Use list_datasets first to find dataset names, then this tool for details.' This clearly establishes the workflow relationship between these two sibling tools and provides practical implementation advice.

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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