cloudscope-mcp
Server Quality Checklist
- Disambiguation4/5
Tools have distinct purposes with clear boundaries: anomaly detection targets spikes, compare_periods is generic, and cost summary aggregates by dimension while top_spending_resources lists individual resources. Minor potential confusion exists between compare_periods and detect_anomalies since both compare time periods, but their specific intents (general comparison vs. spike detection) are differentiated in descriptions.
Naming Consistency4/5Seven tools follow consistent verb_noun pattern (check_, compare_, detect_, get_, list_). However, top_spending_resources breaks convention by using a noun phrase instead of an action verb, and should be renamed to get_top_spending_resources or list_top_spending_resources to match the established pattern.
Tool Count5/5Eight tools is well-scoped for Azure cost management, covering the full analysis lifecycle: current spend visibility (summary, top resources, budgets), trend analysis (compare periods, anomalies), forecasting, and optimization (recommendations). No redundant tools and no obvious bloat.
Completeness4/5Provides comprehensive read-only cost analysis coverage including breakdowns, forecasting, anomaly detection, and recommendations. Minor gaps include lack of budget modification capabilities (create/update budgets) and inability to drill into specific resource cost history beyond the 'top N' list, but core analysis workflows are fully supported.
Average 3.4/5 across 8 of 8 tools scored.
See the tool scores section below for per-tool breakdowns.
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Latest release: v0.1.0
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Tool Scores
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. While it explains the comparison algorithm, it fails to indicate whether this is a safe read-only operation, what data structure is returned, or whether the detection triggers any side effects like notifications.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The single-sentence description is appropriately concise and front-loaded with the action verb 'Find'. Every word serves a purpose in explaining the core mechanism, though it could be slightly more specific by including 'cost' explicitly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple parameter structure (3 primitives, 100% schema coverage) and lack of output schema, the description provides the minimum viable context for the comparison logic. However, it should ideally hint at the return value format (e.g., list of flagged anomalies) to compensate for the missing output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the schema adequately documents all three parameters. The description adds marginal value by illustrating how the 'days' parameter is used in the comparative windows (last N vs previous N), but does not significantly expand on parameter semantics beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool finds 'spending spikes' using a specific comparison methodology (last N days vs previous N days). It effectively conveys the anomaly detection purpose, though it could explicitly mention 'cost' to reinforce the title, and doesn't explicitly differentiate from the sibling 'compare_periods' tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'compare_periods' or 'check_budgets'. It lacks prerequisites (e.g., whether historical data must exist) and exclusions (e.g., when anomalies cannot be detected).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but discloses minimal behavioral traits. It mentions 'based on current trends' indicating the methodology, but fails to state whether this is read-only, what format the forecast returns (currency amount, time series, confidence intervals), or performance characteristics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence is front-loaded with the verb 'Predict' and contains no redundancy. However, given the lack of annotations and output schema, the extreme brevity contributes to under-specification rather than efficient communication.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a forecasting tool with no output schema, the description fails to indicate what the forecast returns (total spend, daily breakdown, confidence ranges). Without annotations to indicate read-only status or safety, the description leaves critical behavioral context undocumented.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, establishing baseline 3. The description adds semantic context by referencing 'next N days' which maps to the 'days' parameter, but does not add format constraints, examples, or semantic relationships between parameters beyond what the schema already documents.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses specific verb 'Predict' with clear resource 'cloud spending' and scope 'next N days based on current trends'. The predictive nature distinguishes it implicitly from historical analysis siblings like get_cost_summary, though it does not explicitly name alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus siblings like compare_periods or detect_anomalies. No prerequisites mentioned (e.g., whether historical data is required for the forecast).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure but fails to indicate read-only safety, rate limits, pagination behavior, or the structure/format of returned recommendations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The single-sentence description is efficient and front-loaded with the verb and resource, containing no redundant text. However, its extreme brevity contributes to gaps in transparency and completeness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
While adequate for a simple two-parameter tool with full schema documentation, the absence of annotations and output schema means the description omits important behavioral and return value context that would aid agent invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, fully documenting the provider constraint and category enum. The description adds minimal semantic value beyond the schema, merely confirming the Azure context without detailing parameter relationships or constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action (Get), resource (cost-saving recommendations), and source system (Azure Advisor). However, it does not explicitly differentiate when to use this versus sibling cost tools like get_cost_summary or check_budgets.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to select this tool over alternatives, prerequisites (such as Azure configuration requirements), or specific scenarios where Azure Advisor recommendations are most appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It only discloses the default date behavior. It fails to mention read-only safety, rate limits, data latency, or what the return structure looks like (e.g., time series vs. single value), which is critical given the absence of an output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with zero filler. The first sentence front-loads the core action (get breakdown) and parameters; the second efficiently communicates default behavior. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description should describe the return value (e.g., aggregated cost records) and any behavioral constraints. It also fails to mention that 'tag' is a valid grouping option. The description is incomplete for a 4-parameter data retrieval tool with rich siblings.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, establishing a baseline of 3. The description mentions grouping by 'service, resource group, or region' but omits the 'tag' option present in the enum. It adds no additional syntax guidance or examples beyond the schema's field descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves a 'cloud spending breakdown' and specifies the grouping dimensions (service, resource_group, region) and date scoping. However, it does not explicitly differentiate this aggregation tool from siblings like 'top_spending_resources' or 'compare_periods'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides concrete operational guidance by stating it 'Defaults to current month if dates omitted,' which helps the agent handle optional parameters. However, it lacks strategic guidance on when to choose this tool over the seven available siblings (e.g., when to aggregate vs. compare vs. forecast).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It establishes the read-only query nature via 'Find' and clarifies granularity ('individual' resources). However, it lacks details on return structure, pagination behavior, or cost calculation methodology that the absence of an output schema makes necessary.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Single, front-loaded sentence with zero waste. Every element ('N', 'most expensive', 'individual', 'Azure', 'time period') earns its place by conveying essential scope and constraints.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequate for a simple three-parameter query tool with complete schema documentation. However, the absence of an output schema and annotations leaves gaps regarding return value structure, safety classifications (read-only confirmation), and error conditions that a complete description should address.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, establishing a baseline of 3. The description conceptually maps 'N' to the limit parameter and 'time period' to days, but does not add syntax, format constraints, or parameter relationships beyond what the schema already documents.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description provides a specific verb ('Find'), resource ('Azure resources'), and scope ('most expensive', 'individual'). The term 'individual' helps distinguish this from aggregate siblings like get_cost_summary, though it doesn't explicitly name alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied by the specific functional description (finding expensive resources vs. checking budgets or forecasting), but there is no explicit guidance on when to prefer this over list_recommendations or detect_anomalies for cost optimization tasks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It successfully discloses output behavior ('per-service absolute and percentage changes'), indicating what calculations are performed. However, it lacks operational details such as whether the operation is read-only, rate limits, or error handling behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence of 12 words. It is front-loaded with the core action ('Compare costs') and every clause contributes essential information about functionality, output format, and scope with zero redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of an output schema, the description appropriately compensates by specifying the return value structure ('per-service absolute and percentage changes'). For a 6-parameter tool with 100% schema coverage, this provides sufficient context for invocation, though operational metadata would enhance it further.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, establishing a baseline of 3. The description adds context that the two date ranges are being compared against each other, reinforcing the semantics of period_a_* and period_b_* parameters, but does not elaborate on syntax or enum meanings beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Compare') with clear resource ('costs') and scope ('between two date ranges'). It distinguishes from siblings like get_cost_summary (single period) or get_cost_forecast (future-looking) by emphasizing the two-period comparison and per-service breakdown.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states what the tool does but provides no explicit guidance on when to use it versus alternatives like get_cost_summary or detect_anomalies. There are no 'when-not' exclusions or prerequisites mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It adds valuable behavioral context by disclosing what data is returned (spend vs limit, forecast, overage risk), compensating for missing output schema. However, it lacks operational details like permission requirements, Azure subscription scoping, or whether this triggers any notifications/alerts.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Single dense sentence with action-front-loaded structure ('Check Azure budget status:'). The colon-separated list efficiently communicates four distinct data points returned. No filler words or redundant explanations. Every token earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple 1-parameter tool with 100% schema coverage but no output schema, the description adequately compensates by detailing the conceptual return values (spend, limits, risk). Missing explicit error handling or subscription scoping details that would be necessary for a 5, but sufficient for agent selection and basic invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 100% coverage with the single 'provider' parameter already documented as 'Cloud provider to query'. The description reinforces the Azure constraint by explicitly mentioning 'Azure budget status' in the text, but does not add semantic depth beyond what the schema and const value already specify. Baseline score appropriate for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Description uses specific verb 'Check' with clear resource 'Azure budget status' and enumerates specific metrics returned (current spend vs limit, percentage used, forecast, overage risk). This clearly distinguishes it from siblings like get_cost_summary (general costs) or detect_anomalies (pattern detection) by focusing on budget limit adherence and overage risk.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when-to-use or alternative recommendations provided. However, the specific metrics listed (percentage used, overage risk) implicitly signal this is for budget monitoring and limit compliance checks, distinguishing it from general cost analysis tools. Agent must infer appropriate use cases from output descriptions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It successfully discloses output format (YYYY-MM-DD) and specific content (today + current/previous month boundaries), though omits timezone or caching behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence of 12 words with zero waste. Front-loaded with action verb and immediately specifies output format and date ranges covered.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple parameter-less tool, description adequately compensates for missing output schema by detailing exact return structure (date format and specific date ranges provided).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Zero parameters present. According to baseline rules for 0-param tools, this scores 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Description uses specific verb 'Returns' and clearly identifies the resource (today's date and month boundaries). It distinguishes from siblings (all cost/budget analysis tools) by being the sole date utility.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides implied usage context (returns date info useful for period comparisons), but lacks explicit guidance on when to use versus alternatives or prerequisites for the sibling cost tools.
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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How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
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