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mcp-knowledge

by io.github.getgapup

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
NameRequiredDescriptionDefault
termNo
asyncNoIf 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.
regionYes
utilizationNo
cloud_providerYes
instance_familyYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
ri_costNo
sourcesNo
warningsNo
on_demand_costNo
break_even_monthsNo
regional_demand_scoreNo
cost_savings_percentageNo
recommended_ri_quantityNo

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint. The description adds that it uses external pricing data and RIPE trends, and mentions output fields. However, it doesn't disclose response time, data freshness, or async behavior beyond the generic async parameter.

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

Conciseness3/5

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

The description is a single paragraph with 4 sentences and a keyword list. It front-loads purpose but includes superfluous keywords. Could be more efficient without losing meaning.

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

Completeness4/5

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

Given the tool has 6 parameters and an output schema (not shown), the description covers purpose, key inputs, outputs, and use case. Lacks details on regional scope, data sources currency, or error handling, but is largely adequate.

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 low (17%). The description lists four of six parameters (cloud_provider, instance_family, region, term), adding context. But it doesn't explain valid values for instance_family or region format, and omits utilization and async. Partially compensates for schema gaps.

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?

The description clearly states it analyzes AWS/Azure pricing with RIPE trends to generate Reserved Instance purchase recommendations. It specifies inputs and outputs, making the tool's purpose distinct from siblings, though it doesn't explicitly differentiate.

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

Usage Guidelines3/5

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

The description says 'Ideal for reducing cloud spend', implying when to use, but provides no guidance on when not to use or alternatives. No exclusions or prerequisites are mentioned.

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

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

Resources