content_provenance
Audit the full data provenance of a content entity — all its enrichment tags with their extraction source, corroboration score, source list and last verification date, plus an entity-level freshness summary. Use this tool before citing or relying on enriched content data in a high-stakes context ...
This record as markdown: /tools/io-github-getgapup-mcp-knowledge/content-provenance.md
What content_provenance does on Mcp Knowledge
AI agents call content_provenance 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. 'video-game-elden-ring') |
entity_type | string | — | Whether the id is a franchise or a work (default: franchise) |
Parameters from the server's own tool schema.
Why content_provenance is rated Low
This tool performs read-only queries of content metadata and provenance information. It inspects and reports on existing enrichment data attributes without modifying, executing, or destructively altering any state. The use case (verifying content reliability before citation) confirms it is a lookup/audit function.
From the tool's definition Tool description explicitly states it 'Audit[s] the full data provenance' and retrieves 'enrichment tags...extraction source, corroboration score, source list and last verification date.' The verb 'Audit' and the focus on inspecting metadata, verification…
Attacks that exploit this kind of access
The rule that runs content_provenance 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_provenance, this is the rule to start with:
content_provenance 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_provenance call is checked against it from then on.
Questions about content_provenance
Audit the full data provenance of a content entity — all its enrichment tags with their extraction source, corroboration score, source list and last verification date, plus an entity-level freshness summary. Use this tool before citing or relying on enriched content data in a high-stakes context (ad targeting, editorial, analysis). Inputs: entity_id (required) and entity_type (franchise or work). It is categorised as a Read tool in the Mcp Knowledge MCP Server, which means it retrieves data without modifying state.
content_provenance 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_provenance: 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_provenance 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_provenance 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_provenance. 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_provenance 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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