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...
This record as markdown: /tools/io-github-getgapup-gapup-mcp/content-similar.md
What content_similar does on Gapup Mcp
AI agents call content_similar to retrieve information from Gapup Mcp 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 semantic similarity search using vector embeddings (pgvector) or tag overlap, returning ranked results with scores. It retrieves and queries existing data without creating, modifying, or deleting anything. Misuse potential is low as it only surfaces informational results about content entities.
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 Gapup Mcp, 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 Gapup Mcp, 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 Gapup Mcp 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 Gapup 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 Gapup Mcp. 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 Gapup MCP server (https://mcp.gapup.io/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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