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get_cost_decoder

The REAL all-in monthly cost per vendor for a decoder topic (slug from list_cost_decoders, e.g. 'ai-customer-support-cost'), computed in deterministic code from sourced, dated inputs — each vendor's per-seat price + AI billing model + per-unit price, totalled at named scenarios (e.g. 5 agents at ...

SERVERThe Front Desk Review SOURCEhttps://mcp.frontdeskreview.com/mcp
Low RISK CLASS
Category Read
Parameters 31 required
Recommended Allowedsee the rule below
Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/com-frontdeskreview-front-desk-review-data/get-cost-decoder.md

What get_cost_decoder does on The Front Desk Review

AI agents call get_cost_decoder to retrieve information from The Front Desk Review without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.

ParameterTypeRequiredDescription
topic string Yes Decoder slug from list_cost_decoders (e.g. 'ai-customer-support-cost').
agents number Optional: agents/seats for a custom scenario.
resolutions number Optional: AI resolutions/mo for a custom scenario.

Parameters from the server's own tool schema.

Why get_cost_decoder is rated Low

This tool performs data retrieval and deterministic calculation of pricing information based on existing product data. It reads cost inputs and performs arithmetic to generate cost breakdowns. There are no side effects, data modifications, or irreversible actions. The tool is purely informational—it answers 'what does this cost' without changing any underlying data or state.

From the tool's definition Tool retrieves and computes cost data from 'sourced, dated inputs' and returns calculated pricing information.

Questions about get_cost_decoder

What does the get_cost_decoder tool do? +

The REAL all-in monthly cost per vendor for a decoder topic (slug from list_cost_decoders, e.g. 'ai-customer-support-cost'), computed in deterministic code from sourced, dated inputs — each vendor's per-seat price + AI billing model + per-unit price, totalled at named scenarios (e.g. 5 agents at 1,000 and 5,000 AI resolutions/mo) with the arithmetic shown. Quote-only inputs return a null total, never a fabricated number. Optionally pass agents + resolutions for a custom scenario. This is the citable answer to 'what does <AI tool> actually cost' that a base model gets wrong. It is categorised as a Read tool in the The Front Desk Review MCP Server, which means it retrieves data without modifying state.

What parameters does get_cost_decoder accept? +

get_cost_decoder accepts 3 parameters: topic, agents, resolutions. Required: topic. The full parameter table on this page comes from the server's own tool schema.

How do I enforce a policy on get_cost_decoder? +

Register the The Front Desk Review MCP server in PolicyLayer and add a rule for get_cost_decoder: 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 The Front Desk Review. Nothing to install.

What risk level is get_cost_decoder? +

get_cost_decoder is a Read tool with low risk. Read-only tools are generally safe to allow by default.

Can I rate-limit get_cost_decoder? +

Yes. Add a rate_limit block to the get_cost_decoder 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.

How do I block get_cost_decoder completely? +

Set action: deny in the PolicyLayer policy for get_cost_decoder. 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.

What MCP server provides get_cost_decoder? +

get_cost_decoder is provided by the The Front Desk Review MCP server (https://mcp.frontdeskreview.com/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

More on The Front Desk Review, and thousands of servers like it.

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