content_ranking
Return the TOP-ranked content entities in a category, by a chosen criterion — the direct answer to superlative / decision queries: 'best video games', 'top RPGs', 'cheapest games', 'best value RPGs', 'best FPS playable right now', 'most popular music artists'. Criteria: critic_score, popularity, ...
This record as markdown: /tools/io-github-getgapup-mcp-knowledge/content-ranking.md
What content_ranking does on Mcp Knowledge
AI agents call content_ranking 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 |
genre | string | — | Optional genre filter, e.g. 'RPG', 'FPS', 'thriller' |
limit | integer | — | Number of ranked results (default 20) |
domain | string | Yes | Content domain to rank within |
year_to | integer | — | Optional latest release year |
criterion | string | — | critic_score (0-100, default) · popularity · price · value (critic score per unit price) |
direction | string | — | desc = best/highest first (default); asc = cheapest/lowest/least first. Defaults to asc for price. |
year_from | integer | — | Optional earliest release year |
available_only | boolean | — | If true, restrict to entities currently available to buy/play (default false) |
Parameters from the server's own tool schema.
Why content_ranking is rated Low
This is a data retrieval and ranking tool with no capability to modify, delete, or execute external operations. It answers superlative queries by querying a content database and returning sorted results. The blast radius of misuse is minimal—an agent could only retrieve ranked lists, not cause damage or unintended system changes.
From the tool's definition Tool returns ranked content entities by criteria (critic_score, popularity, price, value) with filtering options. No mutations, deletions, or side effects occur—it only retrieves and ranks existing data.
Risk signalsBulk/mass operation — affects multiple targets
Attacks that exploit this kind of access
The rule that runs content_ranking 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_ranking, this is the rule to start with:
content_ranking 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_ranking call is checked against it from then on.
Questions about content_ranking
Return the TOP-ranked content entities in a category, by a chosen criterion — the direct answer to superlative / decision queries: 'best video games', 'top RPGs', 'cheapest games', 'best value RPGs', 'best FPS playable right now', 'most popular music artists'. Criteria: critic_score, popularity, price, value (critic score per unit price). direction flips it (asc = cheapest/lowest first). available_only restricts to entities currently buyable. Sliceable by genre and release-year window; every result carries its score, price and source. When to use: an agent must produce a ranked shortlist to support a recommendation, a purchase or a 'what is the best X' decision. It is categorised as a Read tool in the Mcp Knowledge MCP Server, which means it retrieves data without modifying state.
content_ranking accepts 9 parameters: async, genre, limit, domain, year_to, criterion, direction, year_from, available_only. Required: domain. 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_ranking: 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_ranking 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_ranking 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_ranking. 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_ranking 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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