Skip to main content
Glama
rodrigo-orzari

Compare Cloud Costs MCP Server

estimate_workload_architecture

Calculate end-to-end multi-cloud cost benchmarks for realistic full-stack architectures with VMs, managed databases, load balancers, storage, and egress.

Instructions

Calculates end-to-end multi-cloud cost benchmarks for realistic full-stack architectures (VMs + Managed DB + Load Balancers + Storage + Egress).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
workload_idYesPre-modeled workload slug (e.g. three-tier-web-app, ai-rag-pipeline, high-throughput-api, batch-processing)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full behavioral burden. It discloses the scope of components included in the estimate, but says nothing about whether this is a read-only computation, whether benchmarks are cached/precomputed, latency, rate limits, or cost/credential implications of an estimate call.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with the core action stated first and the parenthetical component list adding useful scope at the end. No wasted text, though it is essentially one clause long.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter tool with no annotations and no output schema, the description is adequate: it conveys the output is a multi-cloud cost benchmark. However, it omits any return-format hint and any usage context, leaving gaps an agent must infer.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Only one parameter exists and its schema description has 100% coverage, including example slugs, so the schema already does the heavy lifting. The description adds no further meaning about how workload_id is chosen or what values are valid beyond what the schema states.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (calculates) and a well-scoped resource (end-to-end multi-cloud cost benchmarks for full-stack architectures), enumerating the covered components. It is clear what the tool does, but it never distinguishes itself from the sibling compare_cloud_pricing, which an agent could easily confuse it with.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit when-to-use guidance, no statement of prerequisites, and no routing to or away from the sibling compare_cloud_pricing tool. The only implied usage signal is the 'workload_id' parameter, which is left for the agent to infer.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.