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Data Masking

DataMasking
Read-only

Data Masking: Data Masking redacts sensitive data like emails, phone numbers, payment cards, IP addresses, and dates using placeholder tokens. It returns the sanitized string and detection counts for each pattern found. (Cost: 10 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": {
      "text": "<text>"
    },
    "name": "DataMasking"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYesThe text containing sensitive data to mask
typesNoArray of data types to mask (default: all types)["email","phone","ssn","credit_card","ip_address","url","date"]

Output Schema

NameRequiredDescriptionDefault
dataNo
errorNoError message, null on success
statusNo"ok" on success

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=true, so the safety profile is covered. The description adds genuinely useful context beyond that: it reports the sanitized string plus detection counts per pattern, and it discloses a 10-credit cost, which the annotations do not. It does not state whether the original text is preserved or how placeholders are formatted, leaving some behavioral detail unstated.

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?

Two tightly written sentences that front-load the core operation before the return behavior and cost. Minor waste in the redundant 'Data Masking: Data Masking' preamble that repeats the name and title verbatim.

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

Completeness5/5

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

The tool is simple (one required string input), the schema is fully documented, an output schema exists so return values need no prose explanation, and annotations cover the safety profile. The description's coverage of operation, side-effect semantics, and cost is sufficient for an agent to invoke it correctly.

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 coverage is 100% and both parameters carry descriptions and defaults, so the schema does the heavy lifting. The description's category list loosely maps to the 'types' array but adds no syntax, naming, or behavior detail beyond what the schema already documents. Baseline 3 is appropriate.

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 (redacts) and resource (sensitive data) and enumerates the categories handled: emails, phone numbers, payment cards, IP addresses, and dates. An agent can tell what the tool produces without opening the schema. It stops short of differentiating itself from adjacent siblings such as ProfanityFilter or ContentFilter, so it does not reach a 5.

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?

Usage is only implied by the list of data categories the tool handles; there is no explicit when-to-use statement, no when-not guidance, and no named alternative among the many sibling filtering/redaction tools. An agent must infer that this is for sanitizing text before storage or logging.

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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