Search for content in Alfresco using AFTS query language.
AI agents call search_content to retrieve information from Alfresco MCP Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool retrieves and queries data from the Alfresco content management system without creating, modifying, deleting, or executing operations. Even though it uses AFTS (Alfresco Full Text Search), it remains a read-only search interface. The low severity reflects minimal risk if misused by an AI agent—worst case would be information disclosure of existing content already in the system.
From the tool's definition Tool name 'search_content' and description 'Search for content in Alfresco using AFTS query language' indicate a query/search operation with no side effects. The verb 'search' is explicitly a Read operation per the classification rules.
Documented attack patterns abuse exactly the kind of access search_content gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Alfresco MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for search_content:
{
"version": "1",
"default": "deny",
"tools": {
"search_content": {}
}
} search_content is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Search for content in Alfresco using AFTS query language. It is categorised as a Read tool in the Alfresco MCP Server MCP Server, which means it retrieves data without modifying state.
Register the Alfresco MCP Server MCP server in PolicyLayer and add a rule for search_content: 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 Alfresco MCP Server. Nothing to install.
search_content 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 search_content 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 search_content. 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.
search_content is provided by the Alfresco MCP Server MCP server (stevereiner/python-alfresco-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Alfresco MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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15 Alfresco MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.