Find which files to modify for a task using intelligent file routing. Use this when the user asks
AI agents call predict_coding_approach to retrieve information from In Memoria without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
The tool queries an internal model to predict/recommend which files are relevant for a given task. It reads and returns information (file routing suggestions) without creating, modifying, executing, or deleting any data. Similar in nature to sibling tools like get_pattern_recommendations and get_semantic_insights.
From the tool's definition 'Find which files to modify for a task using intelligent file routing' — this is a lookup/recommendation operation that retrieves predictions without modifying anything.
Documented attack patterns abuse exactly the kind of access predict_coding_approach gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and In Memoria, and nothing reaches the server without passing your rules. This is the rule we recommend for predict_coding_approach:
{
"version": "1",
"default": "deny",
"tools": {
"predict_coding_approach": {}
}
}predict_coding_approach is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Find which files to modify for a task using intelligent file routing. Use this when the user asks. It is categorised as a Read tool in the In Memoria MCP Server, which means it retrieves data without modifying state.
Register the In Memoria MCP server in PolicyLayer and add a rule for predict_coding_approach: 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 In Memoria. Nothing to install.
predict_coding_approach is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the predict_coding_approach 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 predict_coding_approach. 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.
predict_coding_approach is provided by the In Memoria MCP server (pi22by7/in-memoria). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 14 In Memoria tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
14 In Memoria tools catalogued and risk-classified — across an index of 42,500+ MCP servers.