Cost Management MCP

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation3/5
There is significant overlap between tools, particularly 'cost_breakdown', 'cost_get', 'cost_periods', and 'cost_trends', which all seem to retrieve and analyze cost data with subtle distinctions. However, provider-specific tools like 'anthropic_costs' and 'openai_costs' are clearly distinct, and descriptions help differentiate some overlaps, preventing complete confusion.
Naming Consistency4/5
The naming follows a consistent snake_case pattern throughout, with most tools using a clear 'provider_action' or 'cost_action' structure. Minor deviations exist, such as 'cost_get' using a generic verb while others like 'cost_breakdown' are more descriptive, but overall the naming is predictable and readable.
Tool Count5/5
With 10 tools, the count is well-scoped for a cost management server covering multiple providers and analysis dimensions. Each tool appears to serve a specific purpose, such as provider-specific costs, comparisons, and trend analysis, making the set comprehensive without being overwhelming.
Completeness4/5
The tool set covers core cost management workflows, including retrieving costs by provider, comparing providers and periods, analyzing trends, and checking balances. Minor gaps might include operations for setting budgets or configuring providers, but agents can likely work around these with the available tools for monitoring and analysis.
Average 3/5 across 10 of 10 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 3 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
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}
```
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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.

Tool Scores

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 of behavioral disclosure. It mentions 'analyze' and 'insights', which imply read-only operations, but doesn't specify whether this tool requires authentication, has rate limits, returns aggregated data, or handles errors. For a tool with no annotation coverage, this leaves significant behavioral gaps.
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 a single, efficient sentence with no wasted words, making it appropriately concise. However, it's not front-loaded with critical details like scope or differentiation from siblings, which slightly reduces its effectiveness despite the brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of analyzing cost trends with multiple parameters and no output schema, the description is incomplete. It doesn't explain what 'insights' include, how results are formatted, or any dependencies on other tools. With no annotations and an unspecified output, this leaves the agent under-informed for proper usage.
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?
The input schema has 100% description coverage, with clear enums and defaults for all three parameters. The description adds no additional meaning beyond the schema, such as explaining how 'provider' interacts with 'period' or what 'insights' entail. Since schema coverage is high, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Analyze cost trends over time with insights' states a general purpose (analyzing cost trends) but lacks specificity about what resources or data it operates on. It doesn't distinguish itself from sibling tools like 'cost_breakdown' or 'cost_get', making it somewhat vague about its exact function within the toolset.
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?
No guidance is provided on when to use this tool versus alternatives like 'cost_breakdown', 'cost_periods', or provider-specific tools such as 'aws_costs' or 'openai_costs'. The description offers no context about prerequisites, exclusions, or comparative use cases, leaving the agent without direction on tool selection.
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 of behavioral disclosure. It states what the tool does ('compare costs') but doesn't describe how it behaves: whether it's read-only or mutating, what permissions are required, whether it has rate limits, what format the comparison output takes, or if there are any side effects. For a tool with no annotation coverage, this leaves significant gaps in understanding its operational characteristics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise—a single sentence with zero wasted words. It's front-loaded with the core purpose and doesn't include any unnecessary information. This is an example of efficient communication, though it may be too brief for complete understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (5 parameters with nested objects, no output schema, no annotations), the description is insufficient. It doesn't explain what the comparison output looks like, how dates should be formatted, what 'costs' specifically refer to, or how this tool differs from similar siblings. For a tool with this level of parameter complexity and no structured support, the description should provide more contextual information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 40% (2 out of 5 parameters have descriptions). The description 'Compare costs between two time periods' only hints at the 'period1' and 'period2' parameters. It doesn't mention the optional 'provider' parameter, the 'comparisonType' with its enum values, or the 'breakdown' flag. With low schema coverage, the description fails to compensate by explaining parameter meanings or usage.
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 the tool's purpose: 'Compare costs between two time periods'. This is a specific verb ('compare') with a clear resource ('costs') and scope ('two time periods'). However, it doesn't explicitly differentiate from sibling tools like 'cost_trends' or 'provider_compare', which might also involve cost comparisons.
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?
The description provides no guidance on when to use this tool versus alternatives. With multiple sibling tools involving costs (e.g., 'cost_trends', 'provider_compare', 'anthropic_costs'), there's no indication of when this period comparison is preferred over other cost analysis tools. The user must infer usage from the name alone.
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 of behavioral disclosure. It mentions 'optimization tips' as an output, which adds some context beyond basic cost retrieval, but fails to address critical aspects like authentication requirements, rate limits, error handling, or whether this is a read-only operation. For a tool with 6 parameters and no annotations, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose. Every word earns its place by specifying AWS costs, service breakdowns, and optimization tips without redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (6 parameters, no output schema, no annotations), the description is incomplete. It doesn't explain the return format, how optimization tips are structured, or prerequisites like AWS account access. For a tool that likely involves financial data and multiple filtering options, more context is needed to guide effective use.
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 description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no specific parameter semantics beyond implying cost retrieval with breakdowns and tips. It doesn't explain how parameters like 'groupBy' or 'includeForecast' relate to the described outputs, so it meets the baseline for high schema coverage.
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 the tool's purpose: 'Get detailed AWS costs with service breakdown and optimization tips.' It specifies the verb ('Get'), resource ('AWS costs'), and additional outputs ('service breakdown and optimization tips'). However, it doesn't explicitly differentiate from sibling tools like 'cost_breakdown' or 'cost_get,' which appear similar.
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?
The description provides no guidance on when to use this tool versus alternatives. With multiple sibling tools like 'cost_breakdown,' 'cost_get,' and 'provider_compare,' there's no indication of when this AWS-specific tool is preferred or what distinguishes it from generic cost tools.
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 of behavioral disclosure. The description only states what the tool does ('Get detailed cost breakdown') without mentioning any behavioral aspects like authentication requirements, rate limits, data freshness, or what happens when parameters are omitted. For a tool with 6 parameters and no annotation coverage, this is inadequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise - a single sentence that states the core functionality. There's zero wasted language, and it's front-loaded with the essential information. This is an excellent example of efficient communication.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (6 parameters, no annotations, no output schema), the description is insufficient. It doesn't explain what the output looks like, how results are structured, or provide any context about the breakdown format. For a multi-dimensional analysis tool with provider filtering and threshold parameters, more contextual information would be helpful.
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 description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema. The baseline score of 3 is appropriate when the schema does all the parameter documentation work.
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 the tool's purpose: 'Get detailed cost breakdown by multiple dimensions.' It specifies the verb ('Get') and resource ('cost breakdown'), but doesn't distinguish it from sibling tools like 'cost_trends' or 'cost_get' which might have overlapping functionality.
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?
The description provides no guidance on when to use this tool versus alternatives. With multiple sibling tools like 'cost_trends', 'cost_get', and provider-specific tools ('aws_costs', 'openai_costs'), there's no indication of when this multi-dimensional breakdown tool is preferred over those alternatives.
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 of behavioral disclosure. While 'Get' implies a read operation, the description doesn't mention authentication requirements, rate limits, error conditions, response format, or whether this is a real-time query versus cached data. For a cost query tool with no annotation coverage, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that gets straight to the point with zero wasted words. It's appropriately sized for the tool's complexity and front-loads the essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a cost query tool with 5 parameters, no annotations, no output schema, and many sibling tools offering similar functionality, the description is inadequate. It doesn't explain what the tool returns, how it differs from siblings, or important behavioral aspects like authentication, rate limits, or data freshness.
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 description coverage is 100%, so the schema already documents all 5 parameters thoroughly. The description adds minimal value beyond what's in the schema - it mentions 'provider and time period' which aligns with the provider, startDate, and endDate parameters, but doesn't provide additional context about parameter interactions or usage patterns.
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 the verb 'Get' and resource 'cost data' with scope 'for a specific provider and time period', making the purpose immediately understandable. However, it doesn't distinguish this tool from its many siblings (like aws_costs, openai_costs, cost_trends, etc.), which appear to offer similar or overlapping functionality.
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?
The description provides no guidance on when to use this tool versus its many siblings. With tools like aws_costs, openai_costs, cost_trends, cost_breakdown, and provider_compare available, there's no indication of what makes this tool distinct or when it should be preferred over alternatives.
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. It states what the tool does but lacks critical behavioral details such as whether this is a read-only operation, if it requires specific permissions, rate limits, or what the output format looks like. For a tool with multiple parameters and no annotations, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without any unnecessary words. Every part of the sentence contributes directly to understanding the tool's function, making it highly concise and well-structured.
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 the tool's moderate complexity (5 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavioral traits, output format, and usage context. Without annotations or an output schema, the description should provide more completeness to guide the agent effectively.
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 description coverage is 100%, so the schema fully documents all parameters. The description mentions 'model breakdown' and 'token usage', which loosely correspond to 'groupByModel' and 'includeTokenUsage' parameters, but adds no additional semantic context beyond what the schema provides. The baseline score of 3 is appropriate given the high schema coverage.
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 the tool's purpose with a specific verb ('Get') and resource ('detailed Anthropic costs'), including what details are provided ('model breakdown and token usage'). It distinguishes from some siblings like 'aws_costs' or 'openai_costs' by specifying the provider, but doesn't differentiate from generic cost tools like 'cost_breakdown' or 'cost_get' in terms of functionality.
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?
No explicit guidance is provided on when to use this tool versus alternatives. The description does not mention any prerequisites, exclusions, or comparisons to sibling tools like 'cost_breakdown' or 'provider_compare', leaving the agent to infer usage based on the tool name alone.
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 of behavioral disclosure. It mentions 'Get detailed OpenAI costs,' implying a read-only operation, but does not specify authentication needs, rate limits, error handling, or data freshness. For a tool with no annotations, this leaves significant behavioral gaps, though it avoids contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. It directly communicates the tool's function and key outputs, making it easy to parse and understand quickly.
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 the tool's moderate complexity (4 parameters, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks details on output format, error cases, or integration with siblings. Without annotations or output schema, more context would improve completeness, but it meets a basic threshold.
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 description coverage is 100%, so the schema fully documents all parameters. The description adds no additional semantic context beyond implying date range usage and model/token breakdowns, which are already covered by the schema. This meets the baseline for high schema coverage without extra value.
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 the tool's purpose with a specific verb ('Get') and resource ('OpenAI costs'), and specifies the type of data returned ('detailed... with model breakdown and token usage'). However, it does not explicitly differentiate from sibling tools like 'cost_get' or 'cost_breakdown', which might have overlapping functionality, preventing a score of 5.
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?
The description provides no guidance on when to use this tool versus alternatives like 'cost_get' or 'provider_compare' among the siblings. It lacks any context about prerequisites, exclusions, or specific scenarios where this tool is preferred, leaving the agent with minimal usage direction.
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 of behavioral disclosure. It mentions 'check' which implies a read-only operation, but doesn't specify whether this requires authentication, has rate limits, returns real-time or cached data, or what happens if the provider isn't supported beyond the enum. For a tool with no annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste—it directly states the tool's function without unnecessary words. It's appropriately sized for a simple tool and front-loaded with the core purpose, making it easy to parse quickly.
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 the tool's low complexity (one parameter, no output schema, no annotations), the description is minimally adequate but incomplete. It covers the basic purpose but lacks details on usage context, behavioral traits, and how it fits with siblings. Without output schema or annotations, more guidance on expected returns or operational constraints would improve completeness.
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?
The description adds minimal meaning beyond the input schema, which has 100% coverage and fully documents the single 'provider' parameter with an enum. The description implies the tool checks balance for a provider, but doesn't elaborate on what 'balance or credits' means (e.g., monetary, API credits, usage limits). Baseline 3 is appropriate since the schema does the heavy lifting.
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 the tool's purpose with a specific verb ('check') and resource ('remaining balance or credits for a provider'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'aws_costs' or 'openai_costs', which might also relate to provider financial information but with different scopes (e.g., costs vs. balance).
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?
The description provides no guidance on when to use this tool versus alternatives. With sibling tools like 'cost_breakdown', 'cost_trends', and provider-specific cost tools, there's no indication of whether this is for real-time balance checks, historical data, or how it differs from other financial tools. This leaves the agent guessing about appropriate contexts.
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. It states the tool's function but doesn't mention permissions, rate limits, data sources, or output format. For a tool that likely queries financial data, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any fluff or redundancy. It is appropriately sized and front-loaded, making it easy for an agent to parse quickly.
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 the tool's moderate complexity (3 parameters, no output schema, no annotations), the description is minimally adequate but incomplete. It covers the basic purpose but lacks details on behavior, output, or integration with sibling tools, leaving gaps for the agent to 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?
The schema description coverage is 100%, so the schema fully documents the parameters (startDate, endDate, includeChart). The description adds no additional meaning beyond what the schema provides, such as explaining how the comparison is performed or what 'configured providers' entails, resulting in a baseline score.
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 the action ('compare costs') and scope ('across all configured providers'), which is specific and meaningful. However, it doesn't explicitly differentiate from sibling tools like 'cost_breakdown' or 'cost_trends', which might also involve cost analysis, so it doesn't reach the highest score.
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?
The description provides no guidance on when to use this tool versus alternatives like 'cost_breakdown' or 'provider_balance'. It lacks context about prerequisites, exclusions, or comparisons to sibling tools, leaving the agent with minimal usage direction.
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 only states what the tool does, not behavioral traits like permissions needed, rate limits, or what 'status' entails. It lacks details on output format, pagination, or error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words, clearly front-loading the purpose. Every part of the sentence contributes directly to understanding the tool's function.
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 the tool's simplicity (0 parameters, no output schema, no annotations), the description is adequate but minimal. It covers the basic purpose but lacks context about the 'status' meaning or how results are structured, leaving some gaps for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema coverage, so the schema already documents this fully. The description doesn't need to add parameter details, and it appropriately avoids redundancy, earning a baseline score for zero-parameter tools.
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 the verb ('List') and resource ('all configured providers and their status'), making the purpose immediately understandable. It doesn't specifically differentiate from sibling tools like 'provider_balance' or 'provider_compare', which prevents a perfect score.
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?
No guidance is provided about when to use this tool versus alternatives like 'provider_balance' or 'provider_compare'. The description implies usage for listing providers but doesn't specify context, prerequisites, or exclusions.
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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