tengu_ml_predict
Latest ML ensemble prediction for one ticker: blended_score, conviction, decile rank, and per-voter sub-scores. Call this when the user asks 'what does the model think of X' or wants a quantitative score to weigh against fundamentals.
This record as markdown: /tools/tengu-mcp/tengu-ml-predict.md
What tengu_ml_predict does on Tengu
AI agents call tengu_ml_predict to retrieve information from Tengu 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 |
|---|---|---|---|
ticker | string | Yes | Path parameter 'ticker' (required). |
Parameters from the server's own tool schema.
Why tengu_ml_predict is rated Low
Even though tengu_ml_predict 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 tengu_ml_predict safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Tengu, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For tengu_ml_predict, this is the rule to start with:
tengu_ml_predict 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 Tengu, apply this rule, and every tengu_ml_predict call is checked against it from then on.
Questions about tengu_ml_predict
Latest ML ensemble prediction for one ticker: blended_score, conviction, decile rank, and per-voter sub-scores. Call this when the user asks 'what does the model think of X' or wants a quantitative score to weigh against fundamentals. It is categorised as a Read tool in the Tengu MCP Server, which means it retrieves data without modifying state.
tengu_ml_predict accepts 1 parameter: ticker. Required: ticker. The full parameter table on this page comes from the server's own tool schema.
Register the Tengu MCP server in PolicyLayer and add a rule for tengu_ml_predict: 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 Tengu. Nothing to install.
tengu_ml_predict 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 tengu_ml_predict 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 tengu_ml_predict. 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.
tengu_ml_predict is provided by the Tengu MCP server (Hlobo-dev/tengu-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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