Wait for a one_shot deploy to finish and return its final result. one_shot returns a job_token immediately and the LIVE CARD already streams progress and renders the interactive backtest chart itself. Call this ONCE with the token to get the final numbers as TEXT so you can summarize them — it do...
Part of the Quantifyme server.
Free to start. No card required.
AI agents call get_deploy_result to retrieve information from Quantifyme 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 get_deploy_result 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.
{
"version": "1",
"default": "deny",
"tools": {
"get_deploy_result": {}
}
}See the full Quantifyme policy for all 11 tools.
These attack patterns abuse exactly the kind of access get_deploy_result gives an agent. Each links to the full case and the policy that stops it:
Other read tools across the catalogue. The same approach applies to each: allow, with a rate cap to control cost.
Wait for a one_shot deploy to finish and return its final result. one_shot returns a job_token immediately and the LIVE CARD already streams progress and renders the interactive backtest chart itself. Call this ONCE with the token to get the final numbers as TEXT so you can summarize them — it does NOT render another card (no need for get_model_chart). It BLOCKS until the deploy finishes (or ~2.5 min); on timeout it returns ok:false + pending:true — call it again with the same token. IMPORTANT: if source == "community", the deploy used a PRE-EXISTING strategy by @author — tell the user that, share the live_url as the Live dashboard link, and ask whether they'd like to GENERATE A CUSTOM strategy instead. Use the note field as your guide. Args: job_token: the token returned by one_shot. Returns: dict with: ok, stem, model, live_url, symbol, timeframe, channels (list), stats:{ret, wr, pf, n, mdd} (out-of-sample test-split metrics — SHOW THESE), source ("community" | "generated"), author (community username if any), author_url + strategy_url (render @author and "pre-existing strategy" as those Markdown links), community_id, suggest_custom (bool), and note (a ready instruction — follow it). On failure: {ok:false, error} (or {pending:true}).. It is categorised as a Read tool in the Quantifyme MCP Server, which means it retrieves data without modifying state.
Register the Quantifyme MCP server in PolicyLayer and add a rule for get_deploy_result: 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 Quantifyme. Nothing to install.
get_deploy_result 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 get_deploy_result 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 get_deploy_result. 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.
get_deploy_result is provided by the Quantifyme MCP server (https://mcp.quantifyme.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 11 Quantifyme tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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