Composed current view of an entity via a named recipe. Executes a fixed server-side recipe (company_snapshot, etf_snapshot, quote_snapshot, macro_indicator_snapshot, macro_calendar, earnings_snapshot, debt_snapshot) and returns one envelope with freshness, provenance, per-component coverage, and ...
AI agents call get_snapshot to retrieve information from Sugra API without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
| Parameter | Type | Required | Description |
|---|---|---|---|
entity | object | Yes | |
recipe | string | Yes |
Parameters from the server's own tool schema.
This tool retrieves and composes pre-defined data views from a server without modifying, deleting, or executing arbitrary operations. The fixed recipe approach and read-only nature (returning data about entities with metadata like freshness and provenance) classify it as a Read operation.
From the tool's definition Tool executes 'a fixed server-side recipe' and 'returns one envelope' with data composition. The description explicitly lists read-only operations: retrieving snapshots (company_snapshot, etf_snapshot, quote_snapshot, macro_indicator_snapshot, macro_calendar,…
Attacks that exploit this kind of access
Composed current view of an entity via a named recipe. Executes a fixed server-side recipe (company_snapshot, etf_snapshot, quote_snapshot, macro_indicator_snapshot, macro_calendar, earnings_snapshot, debt_snapshot) and returns one envelope with freshness, provenance, per-component coverage, and billing. Composed calls charge the recipe's fixed cost (1-2 units) from the daily quota. status "partial" means an optional component was unavailable - the present components are still trustworthy; honor the freshness block (stale=true means the data aged past its budget). Args: recipe: Recipe name from the fixed manifest. entity: Entity dict from resolve_entity ({"namespace": ..., "ids": ...}). It is categorised as a Read tool in the Sugra API MCP Server, which means it retrieves data without modifying state.
get_snapshot accepts 2 parameters: entity, recipe. Required: entity, recipe. The full parameter table on this page comes from the server's own tool schema.
Register the Sugra API MCP server in PolicyLayer and add a rule for get_snapshot: 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 Sugra API. Nothing to install.
get_snapshot 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_snapshot 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_snapshot. 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_snapshot is provided by the Sugra API MCP server (pypi:sugra-api-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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