Low Risk

teach

Define a logical rule for automatic reasoning. When body conditions hold, head becomes derivable via backward chaining. Use ?-prefixed variables; supports Negation-as-Failure. Example: 'If ?x is human AND NOT god(?x), THEN ?x is mortal'. Side effects: mutates state (additive) — rules remain activ...

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Part of the Nocturnusai MCP server. Enforce policies on this tool with Intercept, the open-source MCP proxy.

AI agents call teach to retrieve information from Nocturnusai without modifying any data. This is common in research, monitoring, and reporting workflows where the agent needs context before taking action. Because read operations don't change state, they are generally safe to allow without restrictions -- but you may still want rate limits to control API costs.

Even though teach only reads data, uncontrolled read access can leak sensitive information or rack up API costs. An agent caught in a retry loop could make thousands of calls per minute. A rate limit gives you a safety net without blocking legitimate use.

Read-only tools are safe to allow by default. No rate limit needed unless you want to control costs.

ai-nocturnus-logic-server.yaml
tools:
  teach:
    rules:
      - action: allow

See the full Nocturnusai policy for all 16 tools.

Tool Name teach
Category Read
Risk Level Low

View all 16 tools →

Agents calling read-class tools like teach have been implicated in these attack patterns. Read the full case and prevention policy for each:

Browse the full MCP Attack Database →

Other tools in the Read risk category across the catalogue. The same policy patterns (rate-limit, allow) apply to each.

What does the teach tool do? +

Define a logical rule for automatic reasoning. When body conditions hold, head becomes derivable via backward chaining. Use ?-prefixed variables; supports Negation-as-Failure. Example: 'If ?x is human AND NOT god(?x), THEN ?x is mortal'. Side effects: mutates state (additive) — rules remain active until explicitly removed. Auth: requires X-Tenant-ID header; RULE_WRITE permission when auth is enabled. Rate-limited per principal. Errors: VALIDATION_ERROR on malformed rules.. It is categorised as a Read tool in the Nocturnusai MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on teach? +

Add a rule in your Intercept YAML policy under the tools section for teach. You can allow, deny, rate-limit, or validate arguments. Then run Intercept as a proxy in front of the Nocturnusai MCP server.

What risk level is teach? +

teach is a Read tool with low risk. Read-only tools are generally safe to allow by default.

Can I rate-limit teach? +

Yes. Add a rate_limit block to the teach rule in your Intercept policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.

How do I block teach completely? +

Set action: deny in the Intercept policy for teach. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.

What MCP server provides teach? +

teach is provided by the Nocturnusai MCP server (nocturnusai-mcp). Intercept sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policies on Nocturnusai

Open source. One binary. Zero dependencies.

npx -y @policylayer/intercept
github.com/policylayer/intercept →
// GET IN TOUCH

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