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-mcp-knowledge/content-enrichment.md
What content_enrichment does on Mcp Knowledge
AI agents call content_enrichment 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 |
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
This tool retrieves and returns existing enriched metadata/tag profiles for a content entity. It performs a read-only lookup with no side effects, modifications, or destructive actions. The inputs are an entity id and type, and the output is descriptive metadata.
From the tool's definition Return the enriched tag profile of a content entity — confidence score, a corroboration score and its full provenance... needs a fine-grained, machine-readable, verifiable characterisation for matching, recommendation, contextual targeting or analysis
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 Mcp Knowledge, 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 Mcp Knowledge, 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 Mcp Knowledge 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 Mcp Knowledge 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 Mcp Knowledge. 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 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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