anchain-data-mcp

Official

by AnChainAI

Overview Schema Source Related Servers Discussions Admin

Python

Remote

fuzzy_search_ofac

Search the OFAC sanctions database for names, addresses, and related fields using fuzzy text matching.

Instructions

Perform fuzzy text matching across the OFAC sanctions database for names, addresses, and related fields.

Args: q: Fuzzy search query for broad text matching (e.g. bank corporation)

Cost: 100 credits

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": {
      "q": "<q>"
    },
    "name": "fuzzy_search_ofac"
  }
}

Input Schema

Name	Required	Description	Default
`q`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 1, 2026

TDQS

A3.9/5.0

Behavior3/5

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

No annotations are provided, so the description carries the behavioral disclosure burden. It does disclose the 'Cost: 100 credits', which is useful context, but it omits other behavioral traits such as result limits, pagination, or error behavior. Since this is a read-only search tool, the missing details are less critical but still represent a gap.

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 compact and front-loaded, with a clear purpose statement, a single parameter explanation, and cost information. Every sentence earns its place, and there is no redundant or filler content.

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?

For a one-parameter tool with an output schema, the description covers the core purpose, parameter semantics, and cost. However, it lacks usage context distinguishing it from the exact-search sibling and does not explain expected result behavior beyond the schema. It is adequate but leaves room for more context.

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 input schema has one parameter 'q' with 0% description coverage, but the description explains its meaning ('Fuzzy search query for broad text matching') and provides a concrete example ('e.g. bank corporation'). This adds significant practical value beyond the bare schema type definition, though it could include format or length constraints.

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 uses a specific verb 'Perform fuzzy text matching' and clearly identifies the resource ('OFAC sanctions database') and the fields searched (names, addresses, related fields). It also distinguishes itself from siblings by emphasizing 'fuzzy' matching and the OFAC scope, which differs from exact search tools like search_ofac and global sanctions tools.

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

Usage Guidelines3/5

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

The description implies use for broad text matching via 'Fuzzy search query for broad text matching', but it does not explicitly state when to use this tool over exact search_ofac or fuzzy_search_global_sanctions. No exclusions or alternative tool recommendations are provided, leaving the usage decision partially ambiguous.

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