Estimate the credits required to run a Disco analysis. Returns required_credits for public (always 0) and private, with private split by whether LLMs are enabled (use_llms=False is faster, use_llms=True adds smarter preprocessing, literature context and a written summary). Also returns per-visibi...
Risk signalsHandles credentials or secrets (api_key)
Part of the Discovery Engine server.
Free to start. No card required.
AI agents invoke discovery_estimate to trigger processes or run actions in Discovery Engine. Execute operations can have side effects beyond the immediate call -- triggering builds, sending notifications, or starting workflows. Rate limits and argument validation are essential to prevent runaway execution.
discovery_estimate can trigger processes with real-world consequences. An uncontrolled agent might start dozens of builds, send mass notifications, or kick off expensive compute jobs. PolicyLayer enforces rate limits and validates arguments to keep execution within safe bounds.
Execute tools trigger processes. Rate-limit and validate arguments to prevent unintended side effects.
{
"version": "1",
"default": "deny",
"tools": {
"discovery_estimate": {
"limits": [
{
"counter": "discovery_estimate_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}See the full Discovery Engine policy for all 14 tools.
These attack patterns abuse exactly the kind of access discovery_estimate gives an agent. Each links to the full case and the policy that stops it:
Other execute tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Estimate the credits required to run a Disco analysis. Returns required_credits for public (always 0) and private, with private split by whether LLMs are enabled (use_llms=False is faster, use_llms=True adds smarter preprocessing, literature context and a written summary). Also returns per-visibility depth caps and accepted file formats. No authentication required — when an API key is supplied, also returns the caller's available credits. Call this before discovery_analyze whenever cost or feasibility is unclear. Args: file_size_mb: Size of the dataset in megabytes. num_columns: Number of columns in the dataset. analysis_depth: Search depth (1=fast, higher=deeper). Used to compute the private-run cost. Default 2. api_key: Disco API key (disco_...). Optional. When provided, the response includes account.available_credits.. It is categorised as a Execute tool in the Discovery Engine MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Discovery Engine MCP server in PolicyLayer and add a rule for discovery_estimate: 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 Discovery Engine. Nothing to install.
discovery_estimate 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 discovery_estimate 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 discovery_estimate. 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.
discovery_estimate is provided by the Discovery Engine MCP server (leap-laboratories/discovery-engine). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 14 Discovery Engine tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
4,600+ MCP servers and 31,000+ tools scanned and risk-classified.