Grade a saved thesis against fundamental momentum since its creation. Pulls revenue / operating-margin / EPS / OCF deltas and aggregates into a score in [-1, +1]. Bull theses are graded by directional alignment, bear by inverse, neutral by closeness-to-flat. The grade is persisted back to the the...
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AI agents call score_thesis_outcome to retrieve information from Valuein — SEC EDGAR Fundamentals & Smart-Money Data without modifying any data. This is common in research, monitoring, and reporting workflows where the agent needs context before taking action. Because read operations don't change state, they are generally safe to allow without restrictions -- but you may still want rate limits to control API costs.
Even though score_thesis_outcome only reads data, uncontrolled read access can leak sensitive information or rack up API costs. An agent caught in a retry loop could make thousands of calls per minute. A rate limit gives you a safety net without blocking legitimate use.
Read-only tools are safe to allow by default. No rate limit needed unless you want to control costs.
{
"version": "1",
"default": "deny",
"tools": {
"score_thesis_outcome": {}
}
}See the full Valuein — SEC EDGAR Fundamentals & Smart-Money Data policy for all 68 tools.
These attack patterns abuse exactly the kind of access score_thesis_outcome gives an agent. Each links to the full case and the policy that stops it:
Other read tools across the catalogue. The same approach applies to each: allow, with a rate cap to control cost.
Grade a saved thesis against fundamental momentum since its creation. Pulls revenue / operating-margin / EPS / OCF deltas and aggregates into a score in [-1, +1]. Bull theses are graded by directional alignment, bear by inverse, neutral by closeness-to-flat. The grade is persisted back to the thesis row; re-call to refresh once new fundamentals land. Note (PR 2): scoring is fundamental-only — does NOT yet include market-price returns. Phase 2 will mix in price data via a partner feed; the response shape is stable.. It is categorised as a Read tool in the Valuein — SEC EDGAR Fundamentals & Smart-Money Data MCP Server, which means it retrieves data without modifying state.
Register the Valuein — SEC EDGAR Fundamentals & Smart-Money Data MCP server in PolicyLayer and add a rule for score_thesis_outcome: 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 Valuein — SEC EDGAR Fundamentals & Smart-Money Data. Nothing to install.
score_thesis_outcome 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 score_thesis_outcome 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 score_thesis_outcome. 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.
score_thesis_outcome is provided by the Valuein — SEC EDGAR Fundamentals & Smart-Money Data MCP server (https://mcp.valuein.biz/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 68 Valuein — SEC EDGAR Fundamentals & Smart-Money Data tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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