Mask sensitive content in codebase documents when AI analysis is unavailable. Applies full, partial, or placeholder masking strategies to prepare fallback architectural decision records.
Apply basic content masking (fallback when AI is not available)
| Name | Required | Description | Default |
|---|---|---|---|
| content | Yes | Content to mask | |
| maskingStrategy | No | Strategy for masking content | full |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds the fallback context but no behavioral detail: no mention of what transformation occurs, whether output is returned, or limitations of 'basic' masking. Annotations provide only readOnly/destructive hints, so the agent is left without a clear model of the tool's effect.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, front-loaded sentence with a parenthetical condition. Every word earns its place and no redundant schema information is repeated.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter tool with full schema coverage, the description plus schema is mostly adequate. However, there is no output schema and no description of the return value or edge behavior, so an agent cannot fully anticipate the result of calling it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers 100% of parameters with descriptions ('Content to mask', 'Strategy for masking content') and an enum, so the baseline is 3. The description adds no parameter information, but the schema already does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a clear verb ('apply') and resource ('basic content masking'), and the parenthetical 'fallback when AI is not available' signals it is the non-AI alternative to sibling tools like generate_content_masking. This distinguishes it from nearby masking tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives an explicit usage condition: use this tool when AI is not available. It doesn't name alternatives or exclusions, but the fallback condition is enough to route an agent. Could be more explicit about when to prefer generate_content_masking.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.