Free-text search across the marketing feature catalog, plan features, integrations, country features, compliance frameworks, competitor positioning, statutory deadlines, local payment methods, and published articles on hellobooks.ai. Queries like "vs Xero", "QuickBooks alternative", "GSTR-3B due"...
AI agents call feature_search to retrieve information from HelloBooks AI MCP Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
| Parameter | Type | Required | Description |
|---|---|---|---|
limit | integer | — | Max results to return (default 20). |
query | string | Yes | Free-text query, e.g. "BAS lodgement", "multi-currency", "vs QuickBooks", "GSTR-3B due", or "UPI invoice cap". |
Parameters from the server's own tool schema.
This tool retrieves and queries published information from a marketing and compliance knowledge base. It has no side effects, does not modify data, does not execute external operations, and does not involve financial transactions. The blast radius of misuse is minimal—an AI agent could retrieve irrelevant or competitive information, but cannot alter systems, commit funds, or cause data loss.
From the tool's definition Tool performs 'free-text search' across 'marketing feature catalog, plan features, integrations, country features, compliance frameworks, competitor positioning, statutory deadlines, local payment methods, and published articles.' The description explicitly…
Risk signalsAccepts freeform code/query input (query)
Attacks that exploit this kind of access
Free-text search across the marketing feature catalog, plan features, integrations, country features, compliance frameworks, competitor positioning, statutory deadlines, local payment methods, and published articles on hellobooks.ai. Queries like "vs Xero", "QuickBooks alternative", "GSTR-3B due", "UPI invoice", "1099 article", or "agentic accounting" surface the matching entry near the top. It is categorised as a Read tool in the HelloBooks AI MCP Server MCP Server, which means it retrieves data without modifying state.
feature_search accepts 2 parameters: limit, query. Required: query. The full parameter table on this page comes from the server's own tool schema.
Register the HelloBooks AI MCP Server MCP server in PolicyLayer and add a rule for feature_search: 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 HelloBooks AI MCP Server. Nothing to install.
feature_search 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 feature_search 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 feature_search. 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.
feature_search is provided by the HelloBooks AI MCP Server MCP server (Meru-Fin-Tech/HelloBooks-MCP-Public). 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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