AIVA MCP Server

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Most tools have distinct purposes, but some potential confusion exists between aiva_get_customer and aiva_search_customers (both retrieve customer data with different approaches) and between shopify_get_product and shopify_get_products (single vs. multiple retrieval). The descriptions help clarify the differences, but agents might occasionally misselect between these pairs.
Naming Consistency5/5
All tools follow a consistent naming pattern: prefix (aiva_ or shopify_) + verb (get, list, search, or actions) + noun. This makes the tool set predictable and easy to understand, with no deviations in style or convention.
Tool Count5/5
With 15 tools, the count is well-scoped for the server's purpose of managing customer and subscription data across AIVA and Shopify. It covers multiple domains without being overwhelming, and each tool appears to serve a specific, useful function.
Completeness4/5
The tool set provides strong coverage for customer, subscription, and product data retrieval, with notable features like churn risk analysis and subscription actions. Minor gaps include no update or delete operations for customers or products, and limited write capabilities beyond subscription actions, but agents can likely work around these for most workflows.
Average 2.9/5 across 15 of 15 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'with filters' but doesn't cover critical aspects like whether this is a read-only operation, pagination behavior (implied by limit/offset but not explained), rate limits, or authentication needs. The description is minimal and leaves key behaviors unspecified.
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 very concise with a single sentence, 'List subscriptions with filters.', which is front-loaded and wastes no words. However, it may be overly terse given the tool's complexity, potentially sacrificing clarity for 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 tool has 4 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain the return format, error handling, or how filters interact, leaving significant gaps for an AI agent to understand and invoke the tool correctly in context.
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 low at 25%, with only the 'status' parameter having an enum description. The description adds no specific meaning beyond 'with filters', which is already implied by the parameters in the schema. It doesn't explain parameter interactions or usage, so it marginally compensates but doesn't fully address the coverage gap.
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 'List subscriptions with filters' clearly states the action (list) and resource (subscriptions), but it's vague about scope and doesn't differentiate from sibling tools like 'aiva_get_subscription' (singular) or 'aiva_subscription_actions'. It specifies 'with filters' which adds some context but remains general.
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 such as 'aiva_get_subscription' for a single subscription or 'aiva_search_customers' for broader queries. The description implies usage for filtered listing but lacks explicit context or exclusions.
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 implies a read-only operation ('get'), but doesn't specify authentication needs, rate limits, error conditions, or the format of returned data. 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 no wasted words. It's front-loaded with the core purpose, making it easy to parse. Every word earns its place, achieving optimal conciseness.
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 no annotations, no output schema, and a description that only states the purpose without behavioral or usage details, the description is incomplete. For a tool that likely returns complex data (affiliate details and stats), more context is needed to help the agent understand what to expect and how to use it 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?
The input schema has 100% description coverage, with clear parameter descriptions for 'affiliateId' and 'code'. The description adds no additional semantic context beyond what the schema provides, such as explaining the relationship between ID and code or usage examples. 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.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool's purpose as retrieving 'affiliate details and stats', which is clear but vague. It specifies the resource (affiliate) and verb (get), but doesn't distinguish it from sibling tools like 'aiva_list_affiliates' or clarify what 'details and stats' entail. This makes it minimally adequate but lacks specificity.
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. It doesn't mention sibling tools like 'aiva_list_affiliates' for listing affiliates or 'aiva_get_referrals' for related data, nor does it specify prerequisites or exclusions. This leaves the agent with no usage context.
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 full burden. It mentions 'List orders with filters', which implies a read-only operation, but doesn't disclose behavioral traits like rate limits, authentication needs, pagination behavior, or what happens with no filters. For a tool with zero annotation coverage, this leaves significant gaps in understanding its operation.
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 with a single sentence 'List orders with filters.', which is front-loaded and wastes no words. Every part earns its place by stating the core action and capability efficiently.
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 complexity (3 parameters, no annotations, no output schema, and low schema coverage), the description is incomplete. It doesn't explain return values, error handling, or usage context, making it inadequate for an agent to invoke the tool correctly without additional assumptions.
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 33% (only 'customerId' has a description), so the description must compensate but adds minimal value. It mentions 'filters' generically, which aligns with the parameters but doesn't explain semantics beyond what the schema provides (e.g., how 'status' enum values affect results). Baseline is 3 due to low coverage, but the description doesn't fully compensate for undocumented parameters like 'limit'.
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 'List orders with filters' clearly states the verb ('List') and resource ('orders'), but it's vague about scope and doesn't distinguish from sibling tools like 'shopify_get_customer' or 'shopify_get_products'. It specifies filtering capability but lacks detail about what 'list' entails (e.g., pagination, date ranges).
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 on when to use this tool versus alternatives like 'aiva_search_customers' or other Shopify tools. The mention of 'filters' implies it's for filtered queries, but there's no explicit context, prerequisites, or exclusions provided to help an agent choose appropriately.
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 sorting by risk score but doesn't cover critical aspects like whether this is a read-only operation, potential rate limits, authentication needs, data freshness, or what the output format looks like. For a tool with no annotations, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded: a single sentence that directly states the tool's purpose. There is no wasted language, and every word earns its place by conveying essential information about what the tool does.
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 retrieving churn risk data, no annotations, and no output schema, the description is incomplete. It doesn't explain the return values, error handling, or behavioral traits like data sources or update frequency. For a tool with significant contextual gaps, this description falls short of providing enough information for 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?
The description adds no parameter semantics beyond what the input schema provides. With 0% schema description coverage, the schema itself documents the parameters ('riskLevel' with enum values and default, 'limit' with default), but the description doesn't explain what these parameters mean or how they affect the results. This meets the baseline of 3 since the schema handles parameter documentation adequately.
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 customers at risk of churning, sorted by risk score.' It specifies the verb ('Get'), resource ('customers at risk of churning'), and sorting behavior. However, it doesn't explicitly differentiate from sibling tools like 'aiva_get_customer' or 'aiva_search_customers', which might also retrieve customer data.
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. It doesn't mention prerequisites, exclusions, or compare it to sibling tools like 'aiva_get_customer' or 'aiva_search_customers', which could be used for similar customer data retrieval. Usage is implied but not explicitly defined.
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 retrieves data ('Get customer profile'), implying a read-only operation, but does not address key aspects such as authentication requirements, rate limits, error handling, or whether it returns partial data if only one parameter is provided. This leaves significant gaps in understanding the tool's behavior.
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 and front-loaded, with two sentences that efficiently state the purpose and output. There is no wasted language, and it directly communicates the core functionality. However, it could be slightly improved by structuring usage guidelines or behavioral details more explicitly, but it remains well-structured for its length.
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 (2 parameters, no output schema, and no annotations), the description is incomplete. It lacks details on behavioral traits, error cases, and how to interpret the return values (e.g., what 'health score' means). Without an output schema, the description should ideally elaborate on the response structure, but it only lists fields without context, leaving the agent with insufficient information for reliable 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?
The description adds minimal value beyond the input schema, which has 100% coverage. It mentions 'by ID or email' but does not explain the semantics, such as whether both parameters can be used together, if one is prioritized, or the format expected. Since the schema already describes the parameters adequately, the baseline score of 3 is appropriate, as the description provides only basic context without enhancing parameter understanding.
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 customer profile by ID or email' specifies the verb (get) and resource (customer profile), and 'Returns customer details, RFM segment, health score, and subscription status' elaborates on the output. However, it does not explicitly differentiate from siblings like 'aiva_search_customers' or 'shopify_get_customer', which could cause confusion.
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. It mentions retrieving by 'ID or email' but does not specify when to choose one over the other, nor does it contrast with sibling tools like 'aiva_search_customers' for broader searches or 'aiva_get_subscription' for focused data. This lack of context leaves the agent without clear usage directives.
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 the action 'Get' but doesn't describe traits like whether it's read-only, requires authentication, has rate limits, or what the output format looks like. The description adds minimal context beyond the basic purpose, missing key behavioral details needed for safe and effective use.
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. It is front-loaded with the core action and scope, making it easy to understand at a glance. Every word earns its place by clarifying the resource and filtering options.
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 (2 parameters, no output schema, no annotations), the description is incomplete. It lacks information on behavioral traits, output format, and usage guidelines. While the schema covers parameters well, the description doesn't address other critical aspects like what the tool returns or how it behaves, making it insufficient for full contextual understanding.
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 parameters 'customerId' and 'days' clearly documented. The description adds no additional meaning beyond what the schema provides, such as explaining parameter interactions or constraints. Since schema coverage is high, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't need to given the schema's completeness.
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 the resource 'upcoming deliveries', specifying it can be for 'a customer or all subscriptions'. It distinguishes from siblings like 'aiva_get_subscription' or 'aiva_list_subscriptions' by focusing on delivery schedules rather than subscription details or lists. However, it doesn't explicitly contrast with all siblings, such as 'aiva_get_customer', which might overlap in customer context.
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. It mentions filtering by customer or all subscriptions but doesn't specify scenarios, prerequisites, or exclusions. For example, it doesn't clarify if this should be used instead of 'aiva_get_subscription' for delivery info or how it relates to 'shopify_get_orders'. This lack of context leaves usage ambiguous.
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 'Get referral history', implying a read-only operation, but doesn't specify if it requires authentication, has rate limits, returns paginated results, or what the output format is. For a tool with no annotations, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
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 unnecessary words. It's front-loaded and appropriately sized, making it easy to parse quickly.
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 has no annotations, no output schema, and incomplete parameter documentation (50% schema coverage), the description is inadequate. It doesn't explain return values, error handling, or behavioral traits like data freshness or access requirements, leaving the agent with insufficient context for reliable 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 50%, with 'affiliateId' documented but 'dateRange' only having an enum without a description in the schema. The tool description doesn't add any parameter details beyond what's implied by 'referral history', such as explaining the date range options or affiliate ID format. Since the schema covers half the parameters, the baseline is 3, but the description doesn't compensate for the uncovered parameter.
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 the resource 'referral history for an affiliate', making the purpose specific and understandable. However, it doesn't explicitly differentiate this tool from its siblings like 'aiva_get_affiliate' or 'aiva_list_affiliates', which might also involve affiliate data, so it doesn't reach the highest 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. For example, it doesn't clarify if this is for historical data only or how it differs from other affiliate-related tools like 'aiva_get_affiliate'. Without any context or exclusions, the agent must infer usage based on 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 what the tool does but does not disclose any behavioral traits such as whether it's a read-only operation, potential rate limits, authentication needs, or error handling. For a tool with no annotations, this is inadequate, as it leaves the agent unaware of important operational aspects beyond the basic function.
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, well-structured sentence: 'Get RFM (Recency, Frequency, Monetary) segment breakdown with customer counts and trends.' It is front-loaded with the core purpose and includes essential details without unnecessary words. Every part of the sentence adds value, making it highly efficient and easy to parse.
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 (involving RFM analysis with trends), no annotations, no output schema, and low parameter schema coverage, the description is incomplete. It does not explain the return format, potential limitations, or how the 'trends' are presented, leaving the agent with insufficient context to fully understand the tool's behavior and output. This is a notable gap for a tool that likely returns structured data.
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 one parameter ('dateRange') with an enum and default value, but schema description coverage is 0%, meaning the schema lacks descriptions. The description does not add any parameter-specific information beyond implying a date range is involved ('segment breakdown'), but it does not explain the parameter's role, the enum values, or how it affects the output. With low schema coverage, the description fails to compensate adequately, 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 tool's purpose: 'Get RFM (Recency, Frequency, Monetary) segment breakdown with customer counts and trends.' It specifies the verb ('Get'), resource ('RFM segment breakdown'), and output details ('customer counts and trends'), which is specific and informative. However, it does not explicitly differentiate from sibling tools like 'aiva_get_churn_risk' or 'aiva_get_customer', which might also involve customer data analysis, so it falls short of 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?
The description provides no guidance on when to use this tool versus alternatives. It does not mention any context, prerequisites, or exclusions, and with sibling tools like 'aiva_get_churn_risk' and 'aiva_search_customers' that might overlap in customer analytics, the lack of differentiation leaves the agent without clear usage instructions. This is a significant gap in helping the agent select the right tool.
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 without behavioral details. It lacks information on permissions, rate limits, error handling, or response format, which are critical for a read operation with two parameters. The description is minimal and doesn't compensate for the absence of annotations.
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 with no wasted words. It is appropriately sized and front-loaded, making it easy to understand quickly.
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 (2 parameters, no output schema, no annotations), the description is incomplete. It doesn't explain return values, error cases, or how to handle the optional parameters, leaving gaps for an AI agent to understand full usage. The description should provide more context to compensate for the lack of structured data.
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 mentions retrieval 'by ID or customer', which aligns with the two parameters in the schema (subscriptionId and customerId). Since schema description coverage is 100%, the baseline is 3, and the description adds minimal value by clarifying the dual lookup options without providing additional syntax or format details beyond the schema.
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 the verb 'Get' and resource 'subscription details', specifying it can retrieve by 'ID or customer'. It distinguishes from sibling 'aiva_list_subscriptions' by focusing on individual retrieval rather than listing, though it doesn't explicitly name this distinction.
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 'aiva_list_subscriptions' or 'aiva_get_customer', nor does it mention prerequisites, exclusions, or context for choosing between subscriptionId and customerId parameters. Usage is implied but not explicitly defined.
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 mentions 'stats' and 'sortable by performance', hinting at read-only behavior and output characteristics, but fails to specify critical details like whether this is a paginated list, what 'stats' include, error conditions, or rate limits. For a list operation with three parameters, 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 front-loads the core purpose ('List affiliates') and adds key features ('with stats, sortable by performance') without unnecessary words. Every part earns its place, making it highly concise and well-structured for quick comprehension.
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 (3 parameters, no annotations, no output schema), the description is incomplete. It lacks details on output format (e.g., what 'stats' include), behavioral traits (e.g., pagination, errors), and usage context compared to siblings. For a list tool with multiple parameters, this leaves the agent under-informed about how to effectively invoke and interpret results.
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 value beyond the input schema, which has 0% description coverage. It implies sorting by 'performance' (mapping to the 'sortBy' parameter with enums like 'referrals' and 'revenue') and hints at filtering by 'stats' (possibly related to 'status'), but doesn't explain parameter meanings, defaults, or interactions. With low schema coverage, the description doesn't adequately compensate, leaving parameters partially undocumented.
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 ('affiliates'), and specifies that it includes 'stats' and is 'sortable by performance', which adds useful detail. However, it doesn't explicitly differentiate from sibling tools like 'aiva_get_affiliate' (which likely retrieves a single affiliate) or 'aiva_get_referrals' (which might focus on referral data specifically), leaving some ambiguity about when to choose this tool over those alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. It doesn't mention sibling tools like 'aiva_get_affiliate' for single affiliates or 'aiva_list_subscriptions' for other list operations, nor does it specify prerequisites, contexts, or exclusions for usage. This lack of comparative guidance leaves the agent to infer usage from tool names 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 filtering capabilities but doesn't address critical aspects like whether this is a read-only operation, potential rate limits, authentication requirements, pagination behavior (implied by limit/offset but not explained), or what the output looks like. For a search tool with 5 parameters and no annotations, 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose. However, it could be more structured by explicitly listing key parameters or usage scenarios. It avoids waste but might benefit from slightly more detail given the tool's complexity.
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 has 5 parameters, no annotations, no output schema, and low schema description coverage (40%), the description is incomplete. It doesn't explain the tool's behavior, output format, error conditions, or how to interpret results. For a search tool in a context with multiple sibling tools, this leaves significant gaps for an AI agent.
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 40% (only 'query' and 'segment' have descriptions), so the description must compensate. It lists filtering by 'segment, subscription status, tags, and more', which partially maps to parameters but doesn't fully explain 'tags' (not in schema), 'limit', 'offset', or the enum values for 'subscriptionStatus'. It adds some value but doesn't adequately cover the 60% gap in schema documentation.
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 ('Search') and resource ('customers'), and specifies filtering capabilities. However, it doesn't explicitly differentiate from sibling tools like 'aiva_get_customer' or 'aiva_list_subscriptions', which might also retrieve customer data. The purpose is clear but lacks sibling distinction.
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 siblings like 'aiva_get_customer' (likely for single customer retrieval) and 'aiva_list_subscriptions' (which might include customer data), there's no indication of when this search tool is preferred or what scenarios it's designed for.
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 lacks critical behavioral details. It mentions actions like 'pause' and 'swap' but doesn't disclose permissions required, whether changes are reversible, rate limits, or what happens on success/failure (e.g., confirmation messages, error states). For a mutation tool with zero 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 that front-loads the purpose ('Perform actions on subscriptions') and lists key actions without redundancy. Every word earns its place, making it easy to scan and understand quickly.
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 a mutation tool with 4 parameters, no annotations, and no output schema, the description is incomplete. It lacks details on behavioral traits (e.g., side effects, error handling), parameter dependencies, and expected outcomes. While it covers the basic purpose, it doesn't compensate for the missing structured data, leaving gaps for safe and effective tool invocation.
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 75%, with three parameters documented (subscriptionId, newProductId, skipDate) and one (action) having an enum but no description. The description adds minimal value beyond the schema by listing action types (pause, resume, skip, swap products), which aligns with the enum, but doesn't explain parameter interactions (e.g., newProductId is only for swap, skipDate only for skip) or provide examples. Baseline 3 is appropriate given the schema does most of the 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 verb ('perform actions') and resource ('subscriptions'), listing specific actions (pause, resume, skip, swap products). It distinguishes from sibling tools like aiva_get_subscription or aiva_list_subscriptions, which are read-only, by indicating mutation capabilities. However, it doesn't specify the exact nature of 'swap products' (e.g., product replacement vs. upgrade), keeping it from 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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing subscription ID from aiva_get_subscription), exclusions (e.g., invalid for cancelled subscriptions), or comparisons to siblings like aiva_get_subscription for read-only access. Usage is implied by the action list but not explicitly stated.
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 full burden. It states it 'gets' details, implying a read operation, but doesn't disclose behavioral traits like authentication requirements, rate limits, error handling, or what specific details are returned. This leaves significant gaps for a tool with no annotation coverage.
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's appropriately sized and front-loaded, making it easy to parse quickly without unnecessary elaboration.
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 no annotations and no output schema, the description is incomplete. It doesn't explain what 'details' are returned, how to handle the optional parameters, or any behavioral context. For a read tool with two parameters, this leaves too many gaps for effective agent 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 both parameters ('customerId' and 'email'). The description adds no additional meaning beyond what the schema provides, such as usage examples or constraints. Baseline 3 is appropriate when 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 verb ('Get') and resource ('Shopify customer details'), making the purpose understandable. It distinguishes from sibling tools like 'aiva_get_customer' by specifying 'Shopify', but doesn't differentiate from other Shopify tools like 'shopify_get_orders' beyond the resource type.
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. It doesn't mention when to prefer this over 'aiva_get_customer' or 'aiva_search_customers', nor does it specify prerequisites or exclusions for usage.
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 mentions 'Search and list' but doesn't clarify if this is a read-only operation, what permissions are needed, whether it supports pagination, rate limits, or error handling. For a tool with 4 parameters and 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 front-loads the core purpose without unnecessary words. Every part of the sentence earns its place by conveying essential information about the tool's function and scope.
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 (4 parameters, no annotations, no output schema), the description is incomplete. It lacks details on behavioral traits, usage context, output format, and doesn't fully address the gaps in parameter documentation, making it insufficient for an agent to confidently invoke the tool.
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 75%, with three parameters having descriptions and one ('limit') having only a default. The description adds no additional parameter semantics beyond implying search and filtering capabilities through 'Search and list' and 'from the connected Shopify store', which doesn't compensate for the partial schema coverage. Baseline 3 is appropriate as the schema does most of the 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 action ('Search and list') and resource ('products from the connected Shopify store'), providing a specific verb+resource combination. However, it doesn't explicitly distinguish this tool from its sibling 'shopify_get_product' (singular), which might retrieve a single product, leaving some ambiguity about sibling differentiation.
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. It doesn't mention sibling tools like 'shopify_get_product' or 'aiva_search_customers', nor does it specify prerequisites, contexts, or exclusions for usage, leaving the agent without clear selection criteria.
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 lacks behavioral details. It doesn't disclose whether this is a read-only operation, authentication requirements, rate limits, error handling, or what 'details' include. The phrase 'Get product details' implies safe retrieval but offers no concrete behavioral context.
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?
Extremely concise with a single, front-loaded sentence that directly states the tool's function. Every word earns its place with no redundancy or fluff, 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?
For a simple retrieval tool with 2 parameters and no output schema, the description is minimally adequate. It covers the core purpose but lacks context on authentication, error cases, return format, or sibling differentiation. Without annotations or output schema, more behavioral transparency 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?
Schema description coverage is 100%, so parameters are documented in the schema. The description adds minimal value by noting these are alternatives ('ID or handle'), but doesn't explain format differences, precedence if both provided, or handle syntax. Baseline 3 is appropriate as 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 verb ('Get') and resource ('product details'), specifying retrieval by 'ID or handle'. It distinguishes from sibling 'shopify_get_products' (plural) by focusing on single product lookup, but doesn't explicitly contrast with other Shopify tools like 'shopify_get_customer' or 'shopify_get_orders'.
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 on when to use this tool versus alternatives is provided. The description doesn't mention when to choose ID vs handle, prerequisites like authentication, or comparison with sibling tools like 'shopify_get_products' for bulk retrieval. Usage is implied but not explicitly defined.
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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