Recommends business / strategy / risk frameworks for a stated problem. Powered by the Jeda.ai · Visual AI framework knowledge graph (~2,100 frameworks across 19 categories, edge-curated). Use when the user describes a business problem ("customer churn rising", "evaluating market entry", "need to ...
Part of the Jeda Ai server.
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AI agents invoke recommend_framework to trigger processes or run actions in Jeda Ai. Execute operations can have side effects beyond the immediate call -- triggering builds, sending notifications, or starting workflows. Rate limits and argument validation are essential to prevent runaway execution.
recommend_framework can trigger processes with real-world consequences. An uncontrolled agent might start dozens of builds, send mass notifications, or kick off expensive compute jobs. PolicyLayer enforces rate limits and validates arguments to keep execution within safe bounds.
Execute tools trigger processes. Rate-limit and validate arguments to prevent unintended side effects.
{
"version": "1",
"default": "deny",
"tools": {
"recommend_framework": {
"limits": [
{
"counter": "recommend_framework_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}See the full Jeda Ai policy for all 5 tools.
These attack patterns abuse exactly the kind of access recommend_framework gives an agent. Each links to the full case and the policy that stops it:
Other execute tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Recommends business / strategy / risk frameworks for a stated problem. Powered by the Jeda.ai · Visual AI framework knowledge graph (~2,100 frameworks across 19 categories, edge-curated). Use when the user describes a business problem ("customer churn rising", "evaluating market entry", "need to assess vendor risk") rather than naming a specific framework. Returns top-N frameworks ranked by fit, each with a concrete reason citing the specific problem signals matched. Input: just the problem statement is enough. Optional faceted filters (persona, regulation, decision_stage) narrow the candidate set. Set limit between 3 and 10 for picker UIs. Pair with generate_framework_analysis to actually run a recommended framework against the user's inputs. Example: { "problem_statement": "We need to decide whether to enter the EU SMB market in Q3", "decision_stage": "decide", "limit": 5 }. It is categorised as a Execute tool in the Jeda Ai MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Jeda Ai MCP server in PolicyLayer and add a rule for recommend_framework: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Jeda Ai. Nothing to install.
recommend_framework is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the recommend_framework rule in your PolicyLayer 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.
Set action: deny in the PolicyLayer policy for recommend_framework. 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.
recommend_framework is provided by the Jeda Ai MCP server (https://mcp.jeda.ai/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 5 Jeda Ai tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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