gapup-mcp

cloud_cost_ri_optimizer

Read-onlyIdempotent

Analyzes AWS and Azure cloud pricing data alongside RIPE regional demand trends to generate Reserved Instance purchase recommendations for CTOs. Inputs include target cloud provider, instance family, region, and desired commitment term. Outputs include cost savings percentage, optimal RI quantity, and regional demand insights. Ideal for reducing cloud spend with data-driven decisions. Keywords: cloud cost optimization, reserved instances, AWS pricing, Azure pricing, RIPE demand trends.

Input Schema

TableJSON Schema

Name	Required	Description
`term`	No
`async`	No	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 timeouts.
`region`	Yes
`utilization`	No
`cloud_provider`	Yes
`instance_family`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes
`ri_cost`	No
`sources`	No
`warnings`	No
`on_demand_cost`	No
`break_even_months`	No
`regional_demand_score`	No
`cost_savings_percentage`	No
`recommended_ri_quantity`	No

TDQS

A3.6/5.0

Behavior2/5

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

Annotations indicate read-only, idempotent, and open-world, so the safety profile is clear. However, the description fails to mention the 'async' parameter defined in the schema, which is a key behavioral aspect for handling slow queries. This omission reduces transparency significantly.

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?

The description is concise (four sentences plus keywords) and front-loaded with the main action. It uses a clear list structure for inputs and outputs. Slight redundancy from the keywords section, but no significant waste.

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?

Given six parameters and an output schema exists, the description covers the essential inputs and high-level outputs. It lacks details on async usage, prerequisites, data freshness, or error handling. Adequate but not comprehensive; the output schema helps but does not fully compensate.

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?

Schema coverage is only 17% (only async has a description). The tool description lists four out of six parameters and explains their role in generating recommendations, adding context beyond the schema. But it omits utilization and async, leaving gaps. Overall, partial but helpful compensation.

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

Purpose5/5

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

The description clearly states the tool analyzes AWS/Azure pricing and RIPE demand to generate Reserved Instance recommendations. It uses specific verbs and resources, and the unique combination of cloud cost optimization and RIPE trends distinguishes it from siblings.

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

Usage Guidelines4/5

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

The description lists inputs (cloud provider, instance family, region, term) and outputs (cost savings, optimal quantity, demand insights), providing clear context for use. However, it does not specify when not to use this tool or mention alternative tools, so it falls short of a 5.

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

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TDQS

C2.5/5.0

Disambiguation2/5

With 271 tools, many have overlapping purposes (e.g., multiple competitor intel tools, multiple financial modelers, multiple ESG auditors). Detailed descriptions help slightly, but the sheer volume creates confusion. Agents would struggle to select the right tool among many similar options.

Naming Consistency1/5

Tool names are wildly inconsistent: mix of English and French, snake_case and short phrases, some very generic (process, run, execute equivalents). No discernible naming convention (e.g., abm_architect vs. boundary_control vs. bp_narratif). This makes it hard to predict tool names.

Tool Count1/5

271 tools is far beyond typical well-scoped servers (3-15). This indicates an unfocused, over-bloated tool surface. Even for a general business intelligence server, this number is excessive and violates the principle of each tool earning its place.

Completeness2/5

Despite the large count, coverage feels scattered. Some domains (e.g., content, competitive intel) have many tools, while others (e.g., supply chain, HR) have gaps. The set lacks a coherent scope; it seems like a dump of many separate tool collections rather than a complete, curated surface.

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