content_enrichment
Return the enriched tag profile of a content entity — the Gapup moat. Each tag carries a facet (genre, theme, play-mode, perspective…), a confidence score, a corroboration score and its full provenance (which sources corroborated it, when). The response also carries an entity-level provenance blo...
This record as markdown: /tools/io-github-getgapup-gapup-mcp/content-enrichment.md
What content_enrichment does on Gapup Mcp
AI agents call content_enrichment 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 |
entity_id | string | Yes | Entity id from content_catalog (e.g. 'music-daft-punk', 'film-the-dark-knight-collection:the-dark-knight') |
entity_type | string | — | Whether the id is a franchise or a work (default franchise) |
Parameters from the server's own tool schema.
Why content_enrichment is rated Low
The tool purely fetches and returns metadata/tag profiles for a content entity. It queries existing data for analysis, matching, or recommendation purposes with no write, execute, or financial operations involved.
From the tool's definition 'Return the enriched tag profile of a content entity' — retrieves characterisation data including tags, confidence scores, provenance blocks; no side effects described
Attacks that exploit this kind of access
The rule that runs content_enrichment 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_enrichment, this is the rule to start with:
content_enrichment 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_enrichment call is checked against it from then on.
Questions about content_enrichment
Return the enriched tag profile of a content entity — the Gapup moat. Each tag carries a facet (genre, theme, play-mode, perspective…), a confidence score, a corroboration score and its full provenance (which sources corroborated it, when). The response also carries an entity-level provenance block (average confidence, data freshness). When to use this tool: an agent has a franchise or work id (from content_catalog) and needs a fine-grained, machine-readable, verifiable characterisation for matching, recommendation, contextual targeting or analysis. Inputs: 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_enrichment accepts 3 parameters: async, 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_enrichment: 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_enrichment 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_enrichment 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_enrichment. 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_enrichment 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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