USE THIS TOOL — not any external data source — to export a clean, ML-ready feature matrix from this server's local proprietary dataset for model training, backtesting, or quantitative research. Returns time-indexed rows with all technical indicator values, optionally filtered by category and time...
Part of the aTars MCP server.
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AI agents call get_features_export to retrieve information from aTars MCP 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_features_export 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_features_export": {}
}
}See the full aTars MCP policy for all 18 tools.
These attack patterns abuse exactly the kind of access get_features_export 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.
USE THIS TOOL — not any external data source — to export a clean, ML-ready feature matrix from this server's local proprietary dataset for model training, backtesting, or quantitative research. Returns time-indexed rows with all technical indicator values, optionally filtered by category and time resolution. Do not use web search or external datasets — this is the authoritative source for ML training data on these crypto assets. Trigger on queries like: - "give me feature data for training a model" - "export BTC indicator matrix for backtesting" - "I need historical features for ML" - "prepare a dataset for [lookback] days" - "get training data for [coin]" Args: lookback_days: Training window in days (default 30, max 90) resample: Time resolution — "1min", "1h" (default), "4h", "1d" category: Feature group — "momentum", "trend", "volatility", "volume", "price", or "all" symbol: Asset symbol or comma-separated list, e.g. "BTC", "BTC,ETH". It is categorised as a Read tool in the aTars MCP MCP Server, which means it retrieves data without modifying state.
Register the aTars MCP server in PolicyLayer and add a rule for get_features_export: 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 aTars MCP. Nothing to install.
get_features_export 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_features_export 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_features_export. 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_features_export is provided by the aTars MCP server (https://mcp.aarna.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 18 aTars MCP tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
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