content_similar
Find content entities similar to a given one. For embedded franchises this uses SEMANTIC vector similarity (pgvector) over the enrichment profile — surfacing entities that feel alike even when their tags differ literally. Falls back to shared enrichment-tag overlap for works or non-embedded entit...
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What content_similar does on Mcp Knowledge
AI agents call content_similar to retrieve information from Mcp Knowledge 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 |
|---|---|---|---|
async | boolean | — | If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client ti |
limit | integer | — | |
entity_id | string | Yes | Entity id from content_catalog |
entity_type | string | — |
Parameters from the server's own tool schema.
Why content_similar is rated Low
This tool performs a read-only similarity search using vector embeddings or tag overlap to retrieve and rank content entities. It has no side effects — it only queries and returns results. Misuse potential is minimal as it only surfaces recommendations/lookalikes.
From the tool's definition Find content entities similar to a given one...surfacing entities that feel alike...Each result carries a similarity score and its entity-level freshness/confidence (verifiable, sourced).
Attacks that exploit this kind of access
The rule that runs content_similar safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Mcp Knowledge, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For content_similar, this is the rule to start with:
content_similar 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 Mcp Knowledge, apply this rule, and every content_similar call is checked against it from then on.
Questions about content_similar
Find content entities similar to a given one. For embedded franchises this uses SEMANTIC vector similarity (pgvector) over the enrichment profile — surfacing entities that feel alike even when their tags differ literally. Falls back to shared enrichment-tag overlap for works or non-embedded entities. Each result carries a similarity score and its entity-level freshness/confidence (verifiable, sourced). When to use this tool: an agent wants recommendations or lookalikes for a franchise or work. Input: an entity_id and its type. It is categorised as a Read tool in the Mcp Knowledge MCP Server, which means it retrieves data without modifying state.
content_similar accepts 4 parameters: async, limit, entity_id, entity_type. Required: entity_id. The full parameter table on this page comes from the server's own tool schema.
Register the Mcp Knowledge MCP server in PolicyLayer and add a rule for content_similar: 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 Mcp Knowledge. Nothing to install.
content_similar 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 content_similar 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 content_similar. 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.
content_similar is provided by the Mcp Knowledge MCP server (https://mcp.gapup.io). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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