util_seeded_train_test_split
Seeded Train Test Split splits items into reproducible train and test sets from bounded caller-supplied values without an external provider. Call Seeded Train Test Split when an agent needs a reproducible decision that another run can independently replay. Returns the deterministic selection plus...
This record as markdown: /tools/io-github-davidmosiah-delx-mcp-a2a/util-seeded-train-test-split.md
What util_seeded_train_test_split does on Delx Mcp A2a
AI agents call util_seeded_train_test_split to retrieve information from Delx Mcp A2a without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
| Parameter | Type | Required | Description |
|---|---|---|---|
seed | string | Yes | Seed supplied to Seeded Train Test Split; used only for this bounded calculation and processed in memory without retention. |
items | array | Yes | Items supplied to Seeded Train Test Split; used only for this bounded calculation and processed in memory without retention. |
test_fraction | number | Yes | Test Fraction supplied to Seeded Train Test Split; used only for this bounded calculation and processed in memory without retention. |
Parameters from the server's own tool schema.
Why util_seeded_train_test_split is rated Low
Even though util_seeded_train_test_split only reads data, uncontrolled read access leaks sensitive information and racks up API costs: an agent caught in a retry loop can make thousands of calls a minute without anyone noticing.
Attacks that exploit this kind of access
The rule that runs util_seeded_train_test_split safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Delx Mcp A2a, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For util_seeded_train_test_split, this is the rule to start with:
util_seeded_train_test_split is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Delx Mcp A2a, apply this rule, and every util_seeded_train_test_split call is checked against it from then on.
Questions about util_seeded_train_test_split
Seeded Train Test Split splits items into reproducible train and test sets from bounded caller-supplied values without an external provider. Call Seeded Train Test Split when an agent needs a reproducible decision that another run can independently replay. Returns the deterministic selection plus seed provenance, population bounds, and the operation-specific outcome for Seeded Train Test Split as versioned deterministic JSON. Limitation: Uses Python pseudorandomness for reproducibility, not cryptography, lotteries with value, security tokens, gambling, or custody. It is categorised as a Read tool in the Delx Mcp A2a MCP Server, which means it retrieves data without modifying state.
util_seeded_train_test_split accepts 3 parameters: seed, items, test_fraction. Required: seed, items, test_fraction. The full parameter table on this page comes from the server's own tool schema.
Register the Delx Mcp A2a MCP server in PolicyLayer and add a rule for util_seeded_train_test_split: 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 Delx Mcp A2a. Nothing to install.
util_seeded_train_test_split 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 util_seeded_train_test_split 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 util_seeded_train_test_split. 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.
util_seeded_train_test_split is provided by the Delx Mcp A2a MCP server (https://api.delx.ai/v1/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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