What is a Content Safety Filter?

LLMs have built-in content safety mechanisms, but these are not sufficient when agents interact with external tools. An agent might be asked to generate content that the model refuses — and instead accomplish it indirectly through tool calls. A code generation tool, a text processing API, or even a file system write can be used to produce content that the LLM would not generate directly.

Content safety filters operate at the tool interaction layer, inspecting both what agents send to tools and what tools return. This covers the gap between model-level safety (which applies to the LLM's own outputs) and tool-level safety (which the MCP protocol does not address). A comprehensive safety posture requires filters at both levels.

The scope of content safety extends beyond obvious categories like violence or hate speech. In enterprise contexts, content safety includes compliance with industry regulations, adherence to brand guidelines, protection of confidential information, and enforcement of acceptable use policies. An agent generating financial advice, medical information, or legal content may need domain-specific content filters.

Content safety filters must balance sensitivity with utility. Over-aggressive filtering blocks legitimate tool usage and frustrates users. Under-aggressive filtering allows policy violations. The optimal approach is configurable, context-aware filtering with clear escalation paths for borderline cases.

Intercept's policy framework supports content safety enforcement through argument validation and output filtering. YAML policies can define content constraints — blocking specific patterns, requiring content classifications, and denying tool calls whose arguments contain policy-violating content. Because policies are defined per-tool and per-server, content safety rules can be tailored to context: stricter for customer-facing tools, more permissive for internal development tools.