auto_repair_loop
Driver: scan a subtree for cook errors, cluster them, route each cluster to the right fix (calls repair_network for structural/expression/flag issues; surfaces fix_shader / fix_reactivity as prompt hand-offs the agent must execute next turn), re-check, and iterate until clean, no-progress (stalle...
This record as markdown: /tools/io-github-pantani-tdmcp/auto-repair-loop.md
What auto_repair_loop does on tdmcp — TouchDesigner MCP server
AI agents invoke auto_repair_loop to trigger actions in tdmcp — TouchDesigner MCP server. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call: builds kicked off, notifications sent, workflows started.
Why auto_repair_loop is rated High
This tool executes automated repair operations on a visual system (calling repair_network, potentially modifying expressions and flags), triggering side effects that depend on detected errors and cluster routing logic. While it defaults to dry-run mode, its capability to execute repairs via repair_network and iterate until convergence or exhaustion classifies it as Execute rather than Write.
From the tool's definition The tool 'scan[s] a subtree for cook errors, cluster[s] them, route[s] each cluster to the right fix' and 'calls repair_network for structural/expression/flag issues'.
Attacks that exploit this kind of access
The rule that runs auto_repair_loop safely
PolicyLayer is an MCP gateway: it sits between your AI agents and tdmcp — TouchDesigner MCP server, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For auto_repair_loop, this is the rule to start with:
auto_repair_loop stays usable, but rate-capped: a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect tdmcp — TouchDesigner MCP server, apply this rule, and every auto_repair_loop call is checked against it from then on.
Questions about auto_repair_loop
Driver: scan a subtree for cook errors, cluster them, route each cluster to the right fix (calls repair_network for structural/expression/flag issues; surfaces fix_shader / fix_reactivity as prompt hand-offs the agent must execute next turn), re-check, and iterate until clean, no-progress (stalled), or max_iterations (exhausted). Dry-run by default — one planning iteration, no writes. The loop CANNOT fix shaders or dead reactivity itself; it points the agent at them via recommended_prompts. Returns {status, iterations[], errors_before, errors_after, remaining[], recommended_prompts[], warnings}. It is categorised as a Execute tool in the tdmcp — TouchDesigner MCP server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the tdmcp — TouchDesigner MCP server MCP server in PolicyLayer and add a rule for auto_repair_loop: 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 tdmcp — TouchDesigner MCP server. Nothing to install.
auto_repair_loop 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 auto_repair_loop 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 auto_repair_loop. 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.
auto_repair_loop is provided by the tdmcp — TouchDesigner MCP server MCP server (@dpantani/tdmcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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