Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add JTCHE/houdini-mcp --skill houdini-mcp-feedbackgit clone --depth 1 https://github.com/JTCHE/houdini-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jtche/houdini-mcp/houdini-mcp-feedback)<a href="https://agentmods.dev/skills/jtche/houdini-mcp/houdini-mcp-feedback"><img src="https://agentmods.dev/badge/skills/jtche/houdini-mcp/houdini-mcp-feedback/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jtche/houdini-mcp/houdini-mcp-feedback"><img src="https://agentmods.dev/badge/skills/jtche/houdini-mcp/houdini-mcp-feedback.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00057 | $0.00817 |
| Opus 5 | $0.00028 | $0.00409 |
| Sonnet 5 | $0.00011 | $0.00163 |
| Haiku 4.5 | $0.00006 | $0.00082 |
Grade A, and why
houdini-mcp-feedback scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Houdini MCP feedback
Answer honestly and thoroughly based on this session's tool use (not regarding the actual Houdini work you produced) — the goal is surfacing your actual process: sequences of calls, places you had to improvise, and anywhere a human had to step in. Skip answering questions that don't apply because you didn't hit that situation. If your answer to one of them is "No issue", skip the question and move on to the next one. Don't skip if your answer takes a few bullets instead of one.
Questions:
- Did you feel like your work leaned more efficient, or friction-y? Were there any tool calls you had to retry, work around, or where the way the results were formatted made you do extra parsing (full node dumps, geometry stats you had to slice yourself, verbose USD listings)?
- What tools did you wish existed so you didn't have to fall back to
execute_houdini_code/execute_hscript, or a multi-step workaround? Specifically call out any generic, reusable primitive you hand-wrote more than once inexecute_houdini_code— e.g. walking a node network, finding a node by type/name/flag, resolving a parm's referencing expressions, listing every node downstream of X, checking cook errors across a subnet. Those are the ones worth turning into real tools; a one-off script built around this scene's own specific data doesn't count against the MCP. - How would you change how a specific tool operates, in order to be more efficient (e.g. more filter/field options so
get_node_info,get_scene_summary,list_children,get_points/get_attrib_valuesreturn tailored results instead of a full dump)? - Did the
docstool earn its keep — didquery/page/nodeget you the right HOM/VEX/parm signature, or did you still guess an API name and have it fail silently? - Did a connection error, plugin stall, or a slow render/cook bite you? Call out whether
batchwould have avoided it, whether the 1-second-between-calls rule cost you round-trips, and whethermonitor_render/get_render_progresstold you enough to avoid blind polling. - Every point a human had to intervene (approve a permission prompt, restart the Houdini MCP plugin, dismiss a Houdini dialog, save the scene, click something in the UI) — for each one: what triggered it, and what alternate sequence/ordering of tool calls on your end would have avoided needing them at all? Be concrete (e.g. "saved the scene defensively before the sim instead of after" / "batched node creation + connection + parm sets into one
batchcall instead of six round-trips"), not just "communicate better." - What state did you find yourself tracking manually across multiple tool calls (current network context, which nodes are dirty/uncooked, display vs render flag, current frame, take, whether a prior mutation left a network half-wired, sim cache validity) that the MCP could instead track or expose directly, so you don't have to infer it from a screenshot or a failed cook?
- One thing you loved, that you wish to be kept exactly as-is.
Keep it tight per point (formatted like /i-have-adhd:i-have-adhd — lead with the concrete takeaway, no throat-clearing) but let questions 6-8 run to a few bullets each if the session actually had that much going on; don't compress real process insight down to one line just to hit a length target.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 22 lines · 57 tokens per session scan A b3122dbd6904
houdini-mcp-feedback is a skill published in the GitHub repository JTCHE/houdini-mcp (3 stars, last pushed 1mo ago), licensed MIT. It adds 57 tokens to every session and 817 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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