platform.batch.call
⚡ ACTION: Execute up to 20 tool calls in a single request with parallel execution (max 10 concurrent). Each call runs the full pipeline independently with its own billing. Returns array of results with per-call status, data, cost, and duration. Save 5x round-trips vs sequential calls. Batch wrapp...
This record as markdown: /tools/io-github-whiteknightonhorse-apibase/platform.batch.call.md
What platform.batch.call does on Apibase
AI agents invoke platform.batch.call to trigger actions in Apibase. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call: builds kicked off, notifications sent, workflows started.
| Parameter | Type | Required | Description |
|---|---|---|---|
calls | array | Yes | Array of tool calls to execute in parallel (max 20) |
max_parallel | integer | — | Max concurrent calls (1-10). Default: 10 |
Parameters from the server's own tool schema.
Why platform.batch.call is rated High
Executes arbitrary batched tool calls with parallelization; blast radius multiplied across up to 20 concurrent operations.
From the tool's definition Execute up to 20 tool calls in a single request with parallel execution, full pipeline independently
Attacks that exploit this kind of access
The rule that runs platform.batch.call safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Apibase, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For platform.batch.call, this is the rule to start with:
platform.batch.call stays usable, but rate-capped: a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Apibase, apply this rule, and every platform.batch.call call is checked against it from then on.
Questions about platform.batch.call
⚡ ACTION: Execute up to 20 tool calls in a single request with parallel execution (max 10 concurrent). Each call runs the full pipeline independently with its own billing. Returns array of results with per-call status, data, cost, and duration. Save 5x round-trips vs sequential calls. Batch wrapper is free — you pay only for individual tool calls (APIbase). It is categorised as a Execute tool in the Apibase MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
platform.batch.call accepts 2 parameters: calls, max_parallel. Required: calls. The full parameter table on this page comes from the server's own tool schema.
Register the Apibase MCP server in PolicyLayer and add a rule for platform.batch.call: 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 Apibase. Nothing to install.
platform.batch.call 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 platform.batch.call 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 platform.batch.call. 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.
platform.batch.call is provided by the Apibase MCP server (apibase-mcp-client). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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