ChartMogul MCP Server

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by chartmogul

Overview Schema Related Servers Score Discussions(1)

Python

Remote

Server Quality Checklist

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Latest release: v0.3.1
Disambiguation4/5
Most tools have distinct purposes targeting specific resources (e.g., customers, metrics, plans) and actions (list, create, update). However, there is some overlap between list_activities and list_customer_activities, which could cause confusion as both retrieve similar activity data but at different scopes (all vs. per-customer). The metrics tools (e.g., mrr_metrics, arr_metrics) are clearly differentiated by the metric type.
Naming Consistency5/5
Tool names follow a highly consistent verb_noun pattern throughout, such as list_customers, create_customer, update_customer, and retrieve_contact. All tools use snake_case uniformly, with verbs like list, create, update, retrieve, add, and get applied predictably across resources. This consistency makes the set easy to navigate and understand.
Tool Count2/5
With 55 tools, the count is excessive for a single server, making it overwhelming and difficult for agents to manage. While the domain (ChartMogul API) is broad, many tools could be consolidated (e.g., separate metrics tools might be combined with parameters). This high number increases complexity and risks tool misselection, indicating poor scoping.
Completeness5/5
The tool set provides comprehensive coverage of the ChartMogul domain, including full CRUD operations for customers, contacts, plans, opportunities, tasks, and more, plus extensive metrics retrieval and filtering capabilities. There are no obvious gaps; agents can perform all core workflows from data ingestion to analysis and CRM management, supported by reference tools like get_cfl_fields.
Average 4/5 across 55 of 55 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
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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.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

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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 full burden. It states this is a creation operation, implying mutation, but lacks details on permissions, side effects, error handling, or rate limits. It mentions the return type ('created plan group object'), but without an output schema, this is vague. More behavioral context is needed for a mutation tool.
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 front-loaded with the core purpose and efficiently lists requirements in a single sentence. It avoids unnecessary fluff, but could be slightly more structured (e.g., separating requirements from returns). Overall, it's concise and to the point.
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, 0% schema coverage, no output schema, and a mutation tool with nested objects, the description is incomplete. It covers basic purpose and parameters but lacks behavioral details, error handling, and comprehensive parameter guidance. For a creation tool in this context, more completeness is needed.
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 0%, so the description must compensate. It specifies required fields ('name' and 'plans') and that all fields go in the 'data' dict, adding meaning beyond the generic schema. However, it doesn't detail data types, constraints, or optional parameters, leaving gaps in 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 action ('Create plan group') and resource ('plan group'), and specifies it's for the ChartMogul API. It distinguishes from siblings like 'create_plan' or 'update_plan_group' by focusing on group creation. However, it doesn't explicitly differentiate from 'list_plan_groups' or 'retrieve_plan_group' beyond the verb, missing some sibling 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 required fields but doesn't specify prerequisites, use cases, or contrast with other tools like 'create_plan' or 'update_plan_group'. This leaves the agent without context for 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?
With no annotations provided, the description carries full burden but only partially discloses behavior. It describes the return structure (attributes object with tags, custom, stripe, clearbit) but lacks details on permissions, rate limits, error handling, or whether it's read-only (implied by 'Retrieve' but not explicit).
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 front-loaded with the core purpose and efficiently details the return structure in a single sentence. It's appropriately sized for the tool's complexity, though the return details could be slightly more 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?
For a read operation with no annotations and no output schema, the description covers the purpose and return format adequately but lacks behavioral context like error cases or authentication needs. It's minimally viable but has clear gaps in completeness.
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?
Schema description coverage is 0%, but the description compensates by specifying the required 'uuid' parameter as 'customer UUID', adding semantic meaning beyond the schema's generic 'string' type. Since there's only one parameter, this provides adequate context.
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 'Retrieve' and resource 'customer attributes (tags and custom attributes)', making the purpose specific. It distinguishes from siblings like 'retrieve_customer' by focusing on attributes rather than customer details, though it doesn't explicitly name alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage 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 'retrieve_customer' or 'list_customers'. The description mentions what it returns but doesn't specify use cases, prerequisites, 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 full burden for behavioral disclosure. It states the tool returns a 'complete note object with all details including timestamps', which provides some output context. However, it doesn't mention authentication requirements, rate limits, error conditions, or whether this is a read-only operation (though 'retrieve' implies read-only). For a tool with zero annotation coverage, this is insufficient behavioral transparency.
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 appropriately concise with two sentences: one stating the purpose and return value, another specifying the required parameter. It's front-loaded with the core functionality. However, the bracketed '[ChartMogul API]' prefix is unnecessary clutter that doesn't add value for tool selection.
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 single-parameter retrieval tool with no output schema, the description provides adequate but minimal context. It covers the purpose, required parameter, and return format. However, without annotations or output schema, it should ideally mention more about authentication, error handling, or what 'complete note object' contains. The lack of sibling differentiation also reduces completeness.
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 description explicitly states 'REQUIRED: uuid (string: note UUID)', which adds crucial semantic information beyond the schema's 0% description coverage. While the schema only shows 'uuid' as a string parameter, the description clarifies it must be a 'note UUID' specifically, not just any UUID. This compensates well for the schema's lack of parameter descriptions.
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 'retrieve' and resource 'specific customer note by UUID', making the purpose unambiguous. It distinguishes from sibling tools like 'list_customer_notes' by specifying retrieval of a single note rather than listing multiple. However, it doesn't explicitly contrast with other retrieval tools like 'retrieve_customer' or 'retrieve_contact'.
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 when to choose this over 'list_customer_notes' for batch operations or other retrieval tools for different resources. The only contextual clue is the REQUIRED field note, which is basic parameter information rather than usage guidance.
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 the tool updates existing events and returns the updated object, but lacks details on permissions, error handling, rate limits, or whether changes are reversible. For a mutation tool with zero annotation coverage, this is insufficient.
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 front-loaded with the core purpose, followed by usage hints and return information in three concise sentences. It avoids unnecessary repetition, though it could be slightly more structured for clarity.
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 mutation tool with no annotations, 0% schema coverage, no output schema, and nested parameters, the description is incomplete. It lacks details on behavioral traits, full parameter documentation, error cases, and output structure, leaving 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 0%, with 1 parameter ('data') documented only as an object with no properties. The description adds value by specifying 'subscription_event object with fields to update' and examples like 'effective_date', but does not fully compensate for the schema gap or provide detailed parameter semantics.
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 ('Update existing subscription event') and resource ('for Custom API data sources'), with specific examples of common updates. It distinguishes from siblings like 'create_subscription_event' by specifying 'existing' and from 'list_subscription_events' by focusing on modification rather than listing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for updating subscription events in Custom API data sources, with examples of common fields. However, it lacks explicit guidance on when to use this tool versus alternatives like 'update_customer' or 'update_plan', and does not mention prerequisites 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?
No annotations are provided, so the description carries full burden. It states this is a creation operation and mentions the return type ('created contact object'), but lacks details on permissions, error handling, rate limits, or side effects. The description doesn't contradict annotations (none exist), but is insufficient for a mutation tool with zero 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: one for the action and parameters, one for the return. It's front-loaded with key information, though the parameter list is somewhat dense and could benefit from bullet points for readability.
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 creation tool with no annotations and no output schema, the description covers the basic purpose and parameters adequately. However, it lacks details on behavioral aspects like authentication needs, error cases, or the format of the returned contact object, leaving gaps in completeness.
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 input schema has 0% description coverage with one complex parameter ('data'). The description compensates by listing all fields within 'data', including required/optional status and data types. This adds significant value beyond the schema, though it doesn't fully explain the structure of nested objects like the 'custom' array.
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 creates a new contact in the ChartMogul API, specifying the verb ('create') and resource ('contact'). It distinguishes from sibling tools like 'list_contacts' and 'update_contact' by focusing on creation, though it doesn't explicitly contrast with other creation tools like 'create_customer'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by listing required and optional parameters, but provides no explicit guidance on when to use this tool versus alternatives like 'create_customer' or 'update_contact'. It mentions the API context but lacks context-specific rules 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 states the tool creates a task and returns an object, but lacks details on permissions, error handling, rate limits, or whether the operation is idempotent. This is insufficient for a mutation tool with zero 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences: one specifying the action and required/optional fields, and another clarifying the data structure and return value. It's front-loaded with key information, though slightly dense.
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 create operation with no annotations and no output schema, the description covers parameters well but lacks behavioral context like authentication needs or error cases. It's minimally adequate given the complexity, but leaves gaps in operational transparency.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage with only one undocumented parameter 'data'. The description fully compensates by detailing all nested fields (customer_uuid, task_details, assignee, due_date, completed_at) with their types, constraints, and required/optional status, adding significant value 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 creates a CRM task in the ChartMogul API, specifying the verb 'Create' and resource 'CRM task'. It distinguishes from siblings like 'create_customer' or 'create_contact' by focusing on tasks, though it doesn't explicitly contrast with them.
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 'update_task' or 'list_tasks'. The description mentions required and optional fields but doesn't explain context or prerequisites for task creation.
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 offers limited behavioral context. It mentions the tool returns an updated contact object but doesn't disclose permissions needed, whether updates are reversible, rate limits, error conditions, or how partial updates are handled. The description doesn't contradict annotations (none exist), but it's insufficient for a mutation tool.
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 efficiently structured with key information front-loaded: API context, action, modifiable fields, custom format example, return value, and required parameters. Every sentence adds value, though it could be slightly more polished (e.g., combining some details).
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 mutation tool with no annotations and no output schema, the description covers parameters well but lacks behavioral context (permissions, side effects) and output details. It's minimally adequate given the complexity but should explain more about the update operation's implications and response format.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by detailing both parameters: uuid (required string identifier) and data (dict containing modifiable fields). It explicitly lists all modifiable fields, explains the custom array format with an example, and clarifies the data structure, adding significant value beyond the bare 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 updates contact information with specific modifiable fields, distinguishing it from sibling tools like create_contact (creation) and retrieve_contact (read-only). However, it doesn't explicitly differentiate from other update tools like update_customer or update_task, which slightly reduces 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 prerequisites (e.g., needing an existing contact UUID), compare with create_contact for new contacts, or explain when to choose update_contact over update_customer for related operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that this is a mutation tool ('Update') and specifies modifiable fields and return value ('Returns updated note object'), which is helpful. However, it lacks details on permissions, error conditions, rate limits, or whether updates are partial/complete, leaving 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 efficiently structured with key information front-loaded: purpose, modifiable fields, and requirements. Every sentence adds value, though it could be slightly more polished (e.g., combining sentences for better flow). No redundant information is present.
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 no annotations, 0% schema coverage, no output schema, and a mutation tool with nested objects, the description does a fair job. It covers purpose, parameters, and return value, but lacks usage guidelines, error handling, and full behavioral context. For a tool with this complexity, it's minimally adequate but has clear gaps.
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?
Schema description coverage is 0%, so the description must compensate. It adds significant value by explaining that 'uuid' is required and 'data' is a dict containing modifiable fields like author_email, text, call_duration, created_at, and updated_at, including data types and formats. This clarifies parameter purposes beyond the bare schema, though it doesn't cover all possible 'data' properties exhaustively.
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 ('Update customer note/call log') and resource ('customer note'), distinguishing it from sibling tools like 'create_customer_note' and 'retrieve_customer_note'. However, it doesn't explicitly differentiate from other update tools like 'update_customer' or 'update_contact', which slightly reduces 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 prerequisites (e.g., needing an existing note UUID), compare to sibling tools like 'create_customer_note' or 'retrieve_customer_note', or specify scenarios where this update is appropriate versus creating a new note.
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 discloses that this is a mutation operation ('Update') and specifies what fields are modifiable (name, plans), which is useful. However, it doesn't describe permissions needed, whether changes are reversible, rate limits, error conditions, or what the 'updated plan group object' contains. For a mutation tool with zero 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 appropriately sized and front-loaded with the core purpose. Each sentence adds value: first states the action, second details modifiable fields with examples, third specifies return value, fourth lists required parameters. There's minimal waste, though the bracketed '[ChartMogul API]' prefix could be omitted as redundant if the server context is already known.
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 mutation tool with 2 parameters, no annotations, and no output schema, the description does an adequate job covering the basics: purpose, parameters, and return type. However, it lacks details on authentication requirements, error handling, side effects, and the structure of the returned object. Given the complexity of updating a resource with nested plans array, more behavioral context would be beneficial.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by explaining both parameters: 'uuid' is required and identifies the plan group, and 'data' is a dict containing modifiable fields (name as string, plans as array of UUIDs). It provides concrete examples of plan UUID format ("pl_123...") and clarifies the structure, adding substantial value beyond the bare 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 action ('Update plan group') and specifies the resource ('plan group'), which is a specific verb+resource combination. It distinguishes itself from siblings like 'create_plan_group' by focusing on modification rather than creation. However, it doesn't explicitly differentiate from other update tools like 'update_plan' or 'update_customer' beyond the resource name.
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, when-not scenarios, or compare with sibling tools like 'retrieve_plan_group' or 'list_plan_groups'. The only implicit usage context is that you need a plan group UUID to update, but this is stated as a requirement rather than guidance.
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. It mentions the return format (complete opportunity object with specific fields) and clarifies amount_in_cents interpretation, which is valuable. However, it doesn't address error handling, authentication needs, rate limits, or whether this is a read-only operation (though 'retrieve' implies read).
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 efficiently structured in two sentences: first states purpose and return format, second specifies the required parameter. Every element earns its place with no redundancy or fluff.
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 single-parameter retrieval tool with no annotations and no output schema, the description covers the basics (purpose, parameter, return fields). However, it lacks context about error cases, authentication, or how this fits within the broader API workflow, leaving gaps for agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage and only 1 parameter, the description fully compensates by explaining the uuid parameter is required, specifies it's a string representing an opportunity UUID, and clarifies it's the retrieval key. This adds complete meaning beyond the bare 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 retrieves a specific opportunity by UUID, which is a specific verb+resource combination. It distinguishes from sibling 'list_opportunities' by focusing on single retrieval rather than listing. However, it doesn't explicitly contrast with other retrieve_* tools like 'retrieve_customer' or 'retrieve_invoice'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when you need a complete opportunity object for a specific UUID, but doesn't explicitly state when to use this vs. 'list_opportunities' or other retrieve_* tools. No guidance on prerequisites or error conditions is provided.
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. It mentions the return format ('complete task object with all details including customer associations and completion status'), which adds value. However, it lacks details on permissions, error handling, rate limits, or whether it's a read-only operation, leaving behavioral gaps for a mutation-heavy sibling 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?
The description is front-loaded with the core purpose, followed by return details and parameter requirement. Every sentence earns its place with no wasted words, making it efficient 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 no annotations, no output schema, and a simple parameter set, the description covers the basics but could be more complete. It explains the return content but doesn't address potential errors, authentication needs, or how it fits into the broader CRM workflow with siblings like 'update_task'.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds significant meaning beyond the input schema, which has 0% coverage. It explicitly defines the 'uuid' parameter as a required string for the task UUID, clarifying its purpose and format. This fully compensates for the schema's lack of descriptions.
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 ('Retrieve') and resource ('specific CRM task by UUID'), and specifies the API context ('ChartMogul API'). It distinguishes from siblings like 'list_tasks' by focusing on a single task retrieval. However, it doesn't explicitly contrast with 'retrieve_customer' or other retrieve siblings, 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when you need a complete task object with details, but doesn't explicitly state when to use this vs. alternatives like 'list_tasks' for multiple tasks or 'retrieve_customer' for customer data. No exclusions or prerequisites are mentioned beyond the required UUID.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that it creates a new opportunity and returns the created object, but lacks details on permissions, error handling, rate limits, or whether the operation is idempotent. It adds basic behavioral context but misses critical mutation-related information.
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 front-loaded with the purpose and efficiently lists parameters in a structured way. However, it could be more concise by avoiding repetition (e.g., 'All fields in data dict' is somewhat redundant) and better organizing the parameter details.
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 complexity (a mutation tool with no annotations and no output schema), the description is partially complete. It explains parameters well but lacks behavioral details like error cases, authentication needs, or response structure. It's adequate but has clear gaps for a creation tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the input schema only has one generic 'data' parameter. The description compensates fully by detailing all required and optional fields (e.g., customer_uuid, amount_in_cents, currency with enums), their types, formats, and constraints, adding significant meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Create sales opportunity') and specifies the resource ('opportunity'), distinguishing it from sibling tools like 'list_opportunities' or 'update_opportunity'. It provides a specific verb and target resource.
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 'update_opportunity' or 'list_opportunities'. The description lacks context about prerequisites, such as needing an existing customer, or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that this is a creation operation (implying mutation) and specifies required/optional fields, adding some behavioral context. However, it doesn't cover permissions, error handling, or rate limits, leaving gaps for a mutation tool.
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 front-loaded with the core purpose, followed by parameter details and return info in a single, efficient sentence. It's appropriately sized with no wasted words, though minor formatting could improve readability.
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 mutation tool with no annotations and no output schema, the description does well on parameters but lacks behavioral details like permissions or error handling. It mentions the return type ('created plan object'), which helps, but overall completeness is adequate with clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description fully compensates by detailing all parameters: it lists required fields (data_source_uuid, name, interval_count, interval_unit) with types and constraints, optional fields (external_id), and clarifies they go in a 'data dict'. This adds significant meaning beyond the bare 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 verb ('Create') and resource ('subscription plan') with specific context ('ChartMogul API'). It distinguishes from siblings like 'create_plan_group' by focusing on individual plans. However, it doesn't explicitly contrast with 'update_plan' or 'list_plans', 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for creating new plans, with no explicit when-not or alternatives. It mentions required and optional fields, which hints at prerequisites, but lacks guidance on when to use this versus similar tools like 'create_plan_group' or 'update_plan'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that this is a creation operation ('Create subscription event') and mentions the return value ('Returns created subscription event object'), but lacks details on permissions, error handling, rate limits, or whether it's idempotent. It adds some behavioral context but not comprehensively.
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 well-structured and front-loaded with the purpose, followed by parameter details and return value. It's concise with no wasted sentences, though it could be slightly more streamlined by integrating the field list more clearly.
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 no annotations, 0% schema coverage, no output schema, and 1 nested parameter, the description does a fair job by specifying fields and return type. However, it lacks details on authentication, error cases, and the 'list above' reference for event_type is unclear, leaving gaps for a creation tool.
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?
The input schema has 0% description coverage with 1 parameter ('data' as an object). The description lists specific fields (e.g., external_id, customer_external_id) that should be included in 'data', adding significant meaning beyond the schema. However, it doesn't fully explain how these fields map to the 'data' object structure or provide examples, leaving some ambiguity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Create subscription event for Custom API data sources' and specifies it's 'Used to track subscription lifecycle changes.' It distinguishes from siblings like 'list_subscription_events' (read) and 'update_subscription_event' (modify) by focusing on creation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context ('for Custom API data sources') and distinguishes from siblings by its creation focus, but does not explicitly state when to use alternatives or any prerequisites. It mentions 'event_type (string: from list above)' which hints at a predefined list, but doesn't specify where to find it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses the tool modifies data (implied by 'Update'), lists modifiable fields, and specifies the return format. However, it doesn't mention authentication requirements, rate limits, error conditions, or whether changes are reversible/destructive.
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 appropriately sized with three sentences: purpose, parameter details, and return format. It's front-loaded with the core action. Minor redundancy exists ('Provide updates in data dict' could be tighter).
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 mutation tool with no annotations and no output schema, the description covers purpose, parameters, and return format adequately. However, it lacks behavioral context like error handling, side effects, or prerequisites. Given the complexity, more completeness would be beneficial.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate fully. It provides essential semantic context: 'uuid' identifies the customer, 'data' contains updates in dict format with specific field examples and nested attribute formatting rules. This adds significant value beyond the bare 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 updates customer attributes with a specific list of modifiable fields. It distinguishes from siblings like 'create_customer' (creation vs update) and 'add_customer_tags' (partial vs comprehensive update), though not all sibling differences are explicitly called out.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when updating customer attributes rather than creating new customers, but doesn't explicitly state when to use this vs alternatives like 'update_contact' or 'add_customer_tags'. No explicit exclusions or prerequisites are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses that this is a mutation operation ('Update'), specifies what fields are modifiable, and mentions the return format ('updated plan object'). However, it lacks details about permissions, rate limits, error conditions, or whether changes are reversible/destructive.
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 efficiently structured in three sentences: purpose statement, parameter details, and return value. Every sentence adds value, though the parameter explanation could be slightly more organized. No wasted words or redundancy.
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 mutation tool with no annotations and no output schema, the description provides good parameter semantics and basic behavioral context but lacks comprehensive details about error handling, authentication requirements, or response structure. It's adequate but has clear gaps given the complexity of updating subscription plans.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by explaining both parameters: 'uuid' as the identifier and 'data' as a dict containing specific modifiable fields (name, interval_count, interval_unit) with detailed constraints and examples. This adds substantial meaning beyond the bare 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 verb ('Update') and resource ('subscription plan') with specific context (ChartMogul API). It distinguishes from siblings like 'create_plan' by focusing on modification rather than creation. However, it doesn't explicitly differentiate from other update tools like 'update_plan_group' beyond the resource name.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when modifying subscription plans and mentions required parameters, but doesn't provide explicit guidance on when to use this versus alternatives like 'update_plan_group' or 'retrieve_plan'. No when-not-to-use scenarios or prerequisites beyond required parameters are stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool modifies a task (implying mutation), lists modifiable fields with constraints (e.g., max 255 chars for task_details, ISO 8601 formats), and mentions the return value (updated task object). However, it lacks details on permissions, error handling, rate limits, or side effects, leaving gaps for a mutation tool.
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 appropriately sized and front-loaded, starting with the API context and action, then listing modifiable fields, required parameters, and return value in a logical order. Every sentence adds value, but it could be slightly more streamlined by integrating the 'Provide in data dict' note more seamlessly.
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 no annotations and no output schema, the description provides good parameter details and return info, but it's incomplete for a mutation tool. It misses behavioral aspects like authentication needs, error cases, or what happens if fields are omitted. For a tool with 2 parameters and nested objects, more context on usage and constraints would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It fully explains both parameters: 'uuid' as a required string identifier and 'data' as a dict containing modifiable fields (task_details, assignee, due_date, completed_at) with specific formats and constraints. This adds significant meaning beyond the bare schema, effectively documenting all parameter semantics.
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 updates a CRM task with specific modifiable fields (task_details, assignee, due_date, completed_at) and identifies the API as ChartMogul. It distinguishes from siblings like 'create_task' (creation vs. update) and 'retrieve_task' (read vs. write), though it doesn't explicitly name these alternatives. The purpose is specific but could be more explicit 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by specifying required parameters (uuid and data) and modifiable fields, suggesting it's for modifying existing tasks. However, it doesn't explicitly state when to use this tool versus alternatives like 'create_task' for new tasks or 'retrieve_task' for viewing, nor does it mention prerequisites or exclusions. Guidelines are implied but not comprehensive.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the return structure (entries array with date, customers, customers_percentage_change, plus summary object) and mentions an example ('customers=382 means 382 active customers'), which adds useful context. However, it lacks details on permissions, rate limits, or error handling, leaving behavioral gaps for a metric retrieval tool.
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 front-loaded with the core purpose and return structure, followed by parameter details and an example. It is appropriately sized with no redundant sentences, though the CFL syntax explanation is somewhat dense. Every sentence adds value, making it efficient for an agent to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (6 parameters, no annotations, no output schema), the description is fairly complete. It explains the purpose, parameters, return format, and provides an example. However, without an output schema, it could benefit from more detail on the 'summary object' structure, and it lacks error or edge-case handling information.
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 schema has 0% description coverage, so the description must compensate. It adds significant semantics: it lists required parameters (start_date, end_date, interval with format/options), optional parameters (geo, plans, filters), explains the CFL syntax for filters, and references 'get_cfl_fields' for field lists. This covers most parameters meaningfully, though some details like geo/plans formats are not elaborated.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: 'Retrieve customer count metrics (total active customers over time).' It specifies the exact resource (customer count metrics) and verb (retrieve), and distinguishes it from sibling tools like 'customer_churn_rate_metrics' or 'mrr_metrics' by focusing on active customer counts over time.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving time-series customer metrics but does not explicitly state when to use this tool versus alternatives like 'all_metrics' or other metric-specific tools. It mentions an optional 'filters' parameter and references 'get_cfl_fields' for field lists, providing some contextual guidance but no clear when/when-not directives or named alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing key behaviors: the default limit (20), a warning about token usage, the return format (plan group objects with uuid, name, plans_count), and pagination details (cursor/has_more). It doesn't mention authentication requirements or rate limits, but covers essential operational aspects.
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 efficiently structured: it starts with the API context and purpose, immediately states the critical limit warning, and details the return format. Every sentence adds value with no redundancy or fluff, making it easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a list operation with no annotations and no output schema, the description is quite complete: it covers purpose, key parameter behavior, return structure, and pagination. It lacks details on error cases or authentication, but provides sufficient context for basic usage given the tool's simplicity.
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 input schema has 0% description coverage, but the description compensates by explaining the 'limit' parameter's default (20) and providing a usage warning. It doesn't detail parameter constraints (e.g., min/max values) or optionality, but adds meaningful context beyond the bare 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 action ('List') and resource ('plan groups'), and explains their purpose ('logical groupings for reporting'). It distinguishes from siblings like 'retrieve_plan_group' (singular) by indicating it returns multiple items. However, it doesn't explicitly differentiate from 'list_plan_group_plans' (which lists plans within a group).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for listing plan groups, but provides no explicit guidance on when to use this versus alternatives like 'list_plans' or 'list_plan_group_plans'. The LIMIT WARNING offers practical advice but doesn't address tool selection. Usage is contextually implied rather than explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing: default limit of 20 with a warning about token usage, pagination behavior, and the structure of returned task objects. It doesn't mention authentication requirements or rate limits, but covers key operational constraints.
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 efficiently structured with clear sections: purpose, limit warning, return format, filters, and example. Each sentence adds value, though the example could be more concise. The information is front-loaded with the most critical details first.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a list tool with 6 parameters and no annotations or output schema, the description provides comprehensive coverage: purpose, constraints, return format, all parameters with semantics, and pagination behavior. The main gap is lack of explicit sibling tool differentiation, but otherwise it's quite complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by explaining all 6 parameters: it lists all filter options (customer_uuid, assignee, due_date_on_or_after, estimated_close_date_on_or_before, completed) and the limit parameter with its default value. It provides format details (ISO 8601 dates, email format for assignee) and semantic meaning for each filter.
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: 'List CRM tasks' from the ChartMogul API. It specifies the resource (CRM tasks) and verb (list), but doesn't explicitly differentiate from sibling tools like 'list_activities' or 'list_opportunities' that also list different resources.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides some usage context through the 'LIMIT WARNING' and filter examples, but doesn't explicitly state when to use this tool versus alternatives like 'list_activities' or 'retrieve_task'. The filter guidance is helpful but not comprehensive about tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the tool mutates data (adds attributes) and returns updated attributes, but does not mention permissions, rate limits, error conditions, or whether the operation is idempotent. It adds some behavioral context (e.g., default source value) but is incomplete for a mutation tool.
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 appropriately sized and front-loaded with the core purpose. Every sentence adds necessary information (e.g., attribute structure, type details, return value). It could be slightly more structured but avoids redundancy and is efficient for the complexity.
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 no annotations and no output schema, the description does well on parameters but lacks completeness for a mutation tool. It explains what the tool does and the input details thoroughly, but does not cover behavioral aspects like side effects, error handling, or response format beyond 'Returns updated custom attributes', leaving gaps in contextual understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, so the description fully compensates by detailing both parameters: 'uuid' (customer UUID) and 'custom_attributes' (array of objects with type, key, value, source). It provides extensive semantics including data types, formats, constraints, and examples, adding significant value beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Add custom attributes to customer') and resource ('customer'), distinguishing it from siblings like 'add_customer_tags' (which adds tags) or 'list_customer_attributes' (which lists attributes). The verb 'add' is precise and the scope is well-defined.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by specifying the ChartMogul API and the need for a customer UUID and attribute array, but does not explicitly state when to use this tool versus alternatives like 'update_customer' or 'list_customer_attributes'. It provides necessary prerequisites but lacks explicit sibling differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and adds valuable behavioral context: it discloses the default limit (20), warns against excessive token usage, and mentions the return type (plan objects). It does not cover all potential behaviors like error handling or pagination, but provides key operational details.
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 appropriately sized and front-loaded with the core purpose, followed by required parameters and behavioral notes. Every sentence adds value, but the structure could be slightly improved by separating requirements and warnings more clearly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, 0% schema coverage, and no output schema, the description does well by covering purpose, parameters, and key behaviors like limits. It could be more complete by explaining the return structure or error cases, but it provides sufficient context for basic tool invocation.
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?
Schema description coverage is 0%, so the description must compensate. It adds meaning by specifying 'uuid' as a required plan group UUID and explaining the 'limit' parameter's default and usage warning. This clarifies semantics beyond the schema's basic types, though it could detail format constraints for UUID.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('plans within specific plan group'), specifying it returns plan objects belonging to the group. It distinguishes from sibling tools like 'list_plans' (general listing) and 'list_plan_groups' (listing groups rather than plans within groups), providing specific differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by specifying it lists plans 'within specific plan group', suggesting it should be used when targeting a particular group. However, it does not explicitly state when to use this tool versus alternatives like 'list_plans' or provide exclusions, leaving some ambiguity for the agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively adds context beyond basic functionality: it warns about LTV values being in integer cents (critical for correct interpretation), explains the LTV formula (LTV = Average Revenue Per User / Customer Churn Rate), describes the return structure (entries array with date, ltv, ltv_percentage_change, plus summary object), and provides an example for clarity. However, it does not cover aspects like rate limits, authentication needs, 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, starting with the core purpose and critical warning about integer cents. Each sentence adds value: formula explanation, return structure, parameter requirements, and an example. There is minimal waste, though it could be slightly more structured (e.g., bullet points for parameters). Overall, it efficiently conveys necessary information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (6 parameters, no annotations, no output schema), the description does a good job of being complete. It explains the tool's purpose, critical data interpretation (integer cents), return format, and parameter semantics. However, it lacks details on error cases, pagination, or authentication requirements, which would enhance completeness for a metric retrieval tool in an API context.
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?
Schema description coverage is 0%, so the description must compensate. It adds significant meaning beyond the schema: it specifies that start_date, end_date, and interval are required and provides format details (YYYY-MM-DD for dates, 'any valid value' for interval). For optional parameters, it explains that filters use 'CFL syntax' and references 'get_cfl_fields' for field lists, adding practical usage context that the schema lacks. The description covers all parameters but could provide more detail on geo and plans.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves Customer Lifetime Value metrics from the ChartMogul API, specifying both the verb ('Retrieve') and resource ('Customer Lifetime Value metrics'). It distinguishes itself from sibling tools like 'arpa_metrics' or 'mrr_metrics' by focusing specifically on LTV calculations, making the purpose highly specific and differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by specifying required parameters (start_date, end_date, interval) and optional ones (geo, plans, filters), but it does not explicitly state when to use this tool versus alternatives like 'all_metrics' or other metric-specific tools. It provides some context through parameter requirements but lacks explicit guidance on tool selection scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses the return format ('complete contact object with all fields including customer associations and custom attributes array'), which is valuable behavioral context. However, it lacks details on error handling, authentication needs, rate limits, or whether it's a read-only operation (though 'retrieve' implies non-destructive).
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 appropriately sized and front-loaded: it starts with the core purpose, then details the return value, and ends with parameter requirements. Every sentence adds value without redundancy, and it uses clear formatting (e.g., 'REQUIRED:' for emphasis).
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 no annotations, no output schema, and low schema coverage (0%), the description is moderately complete. It covers purpose, return format, and parameter semantics adequately, but lacks details on error cases, authentication, or behavioral traits like idempotency. For a retrieval tool with one parameter, it meets minimum viability but has gaps in full context.
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?
Schema description coverage is 0%, so the description must compensate. It adds meaning by specifying that the 'uuid' parameter is a 'contact UUID' (string) and marking it as 'REQUIRED', which clarifies its purpose beyond the schema's generic 'Uuid' title. However, it does not explain format constraints (e.g., UUID version) or provide examples.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Retrieve specific contact by UUID') and resource ('contact'), distinguishing it from siblings like 'list_contacts' (which retrieves multiple contacts) and 'retrieve_customer' (which retrieves a different resource type). It explicitly mentions the ChartMogul API context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by specifying 'by UUID' and listing sibling tools like 'list_contacts' suggests this is for single-contact retrieval. However, it does not explicitly state when to use this versus alternatives like 'list_contacts' or 'retrieve_customer', nor does it mention prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool returns a 'complete plan group object' with specific fields (uuid, name, plans_count, associated plans), which adds useful context about output structure. However, it lacks details on behavioral traits like error handling (e.g., what happens if the UUID is invalid), authentication needs, rate limits, or whether it's a read-only operation, leaving gaps for a mutation-heavy API 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?
The description is appropriately sized and front-loaded: it starts with the core action and resource, followed by return details and parameter requirements in a single, efficient sentence. Every element earns its place without redundancy, 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 complexity (a retrieval tool in a mutation-heavy API with no annotations and no output schema), the description is moderately complete. It covers the purpose, parameter, and return structure, but lacks context on authentication, error handling, or how it fits into broader workflows (e.g., vs. 'list_plan_groups'). The absence of an output schema means the description should ideally detail the return object more thoroughly, which it only partially does.
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 input schema has 1 parameter with 0% description coverage, so the description must compensate. It adds meaning by specifying that 'uuid' is a 'string: plan group UUID' and marking it as 'REQUIRED', which clarifies the parameter's purpose and format beyond the bare schema. However, it does not provide examples or constraints (e.g., UUID format), leaving some ambiguity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Retrieve specific plan group by UUID') and resource ('plan group'), distinguishing it from sibling tools like 'list_plan_groups' (which lists multiple) and 'retrieve_plan' (which retrieves individual plans). It explicitly mentions the API context ('ChartMogul API') and the required parameter, making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: it specifies that this tool is for retrieving a 'specific plan group by UUID' rather than listing multiple groups. It implicitly distinguishes from 'list_plan_groups' by focusing on single retrieval, but does not explicitly state when to use alternatives or any exclusions, such as prerequisites or error conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that this is a mutation tool ('Update') and specifies the return format ('Returns updated opportunity object'), but lacks details on permissions, error handling, rate limits, or side effects. The description adds some behavioral context but is incomplete for a mutation tool.
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 front-loaded with the core purpose and modifiable fields, followed by parameter requirements. It is appropriately sized with no redundant sentences, though the list of modifiable fields is lengthy but necessary. Every sentence earns its place by providing critical information.
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 no annotations and no output schema, the description covers parameters well and specifies the return type. However, for a mutation tool, it lacks details on authentication, error cases, or behavioral nuances. It is moderately complete but has gaps in safety and operational context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It fully documents both parameters: 'uuid' (string, required) and 'data' (dict, required), including detailed semantics for 'data' with field types, formats, enums, and constraints (e.g., 'amount_in_cents: integer', 'currency: "USD", "EUR", "GBP"'). This adds significant value beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Update') and resource ('sales opportunity') with specific context ('ChartMogul API'), distinguishing it from siblings like 'create_opportunity' (creation) and 'retrieve_opportunity' (read). It explicitly lists modifiable fields, making the purpose highly specific and actionable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for modifying existing opportunities (via 'uuid' requirement) but does not explicitly state when to use this tool versus alternatives like 'create_opportunity' or 'update_contact'. It provides clear context for updating sales data but lacks explicit exclusions or comparison to sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing critical behavioral traits: the ARPA values are in integer cents requiring division by 100, the return format (entries array with date, arpa, arpa_percentage_change plus summary object), and provides a concrete example (arpa=84767 means $847.67). However, it doesn't mention rate limits, authentication requirements, or pagination 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 appropriately sized and front-loaded with the core purpose and critical warning about integer cents. Every sentence adds value: purpose, critical conversion note, formula, return format, parameter requirements, optional parameters with syntax, and example. Minor redundancy exists in explaining the cents conversion multiple times.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 6-parameter tool with no annotations and no output schema, the description provides excellent coverage: clear purpose, behavioral details (return format, data interpretation), complete parameter semantics, and an example. The main gap is lack of explicit guidance on when to use versus sibling metrics tools, but otherwise it's quite complete for the complexity level.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by explaining all 6 parameters: it specifies required parameters (start_date, end_date, interval with format and allowed values), optional parameters (geo, plans, filters), provides the filter syntax format (CFL syntax), and references get_cfl_fields for field lists. The description adds substantial meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Retrieve Average Revenue Per Account metrics') and resource (ChartMogul API metrics), distinguishing it from sibling tools like arr_metrics, asp_metrics, or mrr_metrics by focusing specifically on ARPA. It provides the exact formula (ARPA = Total MRR / Total Customers) to clarify what the metric represents.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context through the required parameters (date range and interval) and mentions an optional filter syntax, but doesn't explicitly state when to use this tool versus alternatives like mrr_metrics or customer_count_metrics. It references get_cfl_fields for filter field lists, which provides some guidance but not explicit comparison to sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does well by disclosing critical behavioral traits: ARR values are in integer cents (requiring division by 100), the return format includes an entries array and summary object, and it provides a concrete example. However, it lacks details on rate limits, authentication needs, 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with key information (purpose and critical note about cents), followed by details on returns, parameters, and an example. It is appropriately sized with no wasted sentences, though the formatting could be slightly more structured for readability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (6 parameters, no annotations, no output schema), the description is largely complete: it covers purpose, critical data format, return structure, all parameters with semantics, and an example. It lacks only minor details like pagination or error scenarios, but is sufficient for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate, which it does excellently. It explains all parameters: required ones (start_date, end_date, interval with allowed values), optional ones (geo, plans, filters with syntax guidance and reference to another tool), and provides an example for clarity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves Annual Recurring Revenue metrics from the ChartMogul API, specifying the exact calculation (ARR = MRR × 12) and distinguishing it from sibling tools like 'mrr_metrics' and 'all_metrics' by focusing specifically on ARR.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by specifying required parameters and mentioning an optional 'filters' parameter that references 'get_cfl_fields' for field lists, but it does not explicitly state when to use this tool versus alternatives like 'mrr_metrics' or 'all_metrics'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the creation action, specifies required and optional fields, details data formats (e.g., ISO standards, custom attribute types), and mentions the return value ('Returns created customer object'). However, it lacks information on error handling, rate limits, or authentication requirements, which are important for a mutation tool.
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 front-loaded with the core purpose and required parameters, followed by optional details in a structured list. It is appropriately sized for the complexity, but could be slightly more concise by avoiding repetition (e.g., 'All fields in data dict' is somewhat redundant given the context). Overall, it efficiently conveys necessary information without excessive verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (mutation with many nested fields), no annotations, and no output schema, the description is largely complete. It covers purpose, parameters, data formats, and return value. However, it lacks details on error cases, side effects, or integration context (e.g., how this fits with sibling tools), leaving minor gaps in full contextual understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, with only one generic parameter ('data'), so the description fully compensates by detailing all nested fields within 'data'. It clearly distinguishes required vs. optional parameters, provides format specifications (e.g., 'ISO-3166 alpha-2', 'ISO 8601'), and explains custom attribute structures, adding substantial meaning beyond the minimal input schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Create new customer') and resource ('customer'), distinguishing it from sibling tools like 'update_customer' or 'list_customers'. It explicitly identifies the API context ('ChartMogul API'), making the purpose unambiguous and distinct.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by specifying required and optional parameters, but does not explicitly state when to use this tool versus alternatives like 'update_customer' or 'create_contact'. No guidance is provided on prerequisites or exclusions, leaving usage context partially inferred rather than clearly defined.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses the tool returns an entries array with specific fields (date, customer_churn_rate, customer_churn_rate_percentage_change) plus a summary object, and provides an example interpretation (customer_churn_rate=3.9 means 3.9%). However, it doesn't mention rate limits, authentication requirements, error conditions, or whether this is a read-only operation (though 'retrieve' implies it).
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 efficiently structured with the core purpose first, followed by parameter details and an example. Every sentence adds value, though it could be slightly more front-loaded by moving the example to the end. The length is appropriate for a 6-parameter tool with complex filtering options.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a metrics retrieval tool with no annotations and no output schema, the description does well by explaining the return structure (entries array with specific fields plus summary object) and providing parameter details. However, it doesn't fully address behavioral aspects like pagination, error handling, or authentication requirements that would be important for an API tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by explaining all 6 parameters: it identifies required vs. optional parameters, specifies date format (YYYY-MM-DD), lists interval options (day, week, month, quarter, year), explains what geo and plans parameters do, and provides detailed syntax for filters (CFL syntax with reference to get_cfl_fields). This adds substantial meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves customer churn rate metrics as a percentage, specifies the exact formula (Customer Churn Rate = (Churned Customers / Total Customers at Start) × 100), and distinguishes it from sibling tools like 'mrr_churn_rate_metrics' by focusing on customer metrics rather than MRR metrics. The purpose is specific and well-differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about required parameters (start_date, end_date, interval) and optional filters, but doesn't explicitly state when to use this tool versus alternatives like 'customer_count_metrics' or 'mrr_churn_rate_metrics'. It mentions using 'get_cfl_fields' for filter field lists, which is helpful guidance but not full alternative comparison.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the return format ('Returns import summary with created invoices and errors') and mentions it's for historical data import, but doesn't address permission requirements, rate limits, idempotency, or error handling details that would be important for a data import operation.
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 appropriately front-loaded with the core purpose, followed by detailed parameter specifications. While comprehensive, the nested object specifications are lengthy but necessary given the complex data structure. Every sentence serves a clear purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex mutation tool with no annotations and no output schema, the description does well by explaining the return format and providing extensive parameter details. However, it could better address behavioral aspects like error conditions or idempotency given the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite 0% schema description coverage, the description comprehensively explains both parameters: 'data' is detailed as 'invoices data structure' with full specification of nested objects and arrays, and 'uuid' is clarified as 'customer UUID'. This fully compensates for the schema's lack of documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Import invoices for customer'), target resource ('historical billing data'), and distinguishes from siblings by specifying it's for 'Custom API data sources' unlike other invoice-related tools like list_invoices or retrieve_invoice. The verb 'import' with 'add historical billing data' provides precise scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states 'Use for Custom API data sources' which provides clear context for when to use this tool. However, it doesn't mention when NOT to use it or name specific alternatives among the many sibling tools, preventing a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It effectively discloses key behavioral traits: the default limit of 20 items, a warning against excessive token usage, pagination details (cursor/has_more), and example filter usage. However, it does not cover error conditions or authentication requirements.
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 appropriately sized and front-loaded, starting with the core purpose and immediately addressing critical warnings. Each sentence adds value, though it could be slightly more structured for readability (e.g., separating warnings from return details).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description does well by covering purpose, parameters, behavioral traits (limits, pagination), and return values. It lacks details on error handling or authentication, but for a list tool with good parameter coverage, it is largely complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It adds significant meaning beyond the schema by explaining all four parameters: data_source_uuid, external_id, and system as filters, and limit with its default and warning. It also provides example values for system and interval_unit, enhancing understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: listing subscription plans with pricing/billing intervals. It specifies the resource (plans) and distinguishes it from siblings like retrieve_plan (single plan) and create_plan (creation). The mention of ChartMogul API provides context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for listing plans with filtering options, but does not explicitly state when to use this tool versus alternatives like retrieve_plan or list_plan_groups. It provides some context with filters but lacks explicit guidance on tool selection among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior by explaining the calculation, interpreting result ranges (normal range -10% to +10%), and detailing the return structure (entries array with date, mrr_churn_rate, mrr_churn_rate_percentage_change, plus summary object). However, it does not cover aspects like rate limits, authentication requirements, 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with key information (purpose, formula, result interpretation), but it includes some redundancy (e.g., repeating that negative values are good) and could be slightly more concise. Most sentences earn their place by providing essential details, though the example at the end could be integrated more efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 6 parameters, 0% schema coverage, and no output schema, the description is largely complete. It covers the purpose, parameters, result interpretation, and return structure. However, it lacks information on error cases, pagination, or API-specific constraints (e.g., date range limits), leaving minor gaps given the complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Given 0% schema description coverage, the description compensates fully by explaining all parameters. It specifies required parameters (start_date, end_date, interval with format YYYY-MM-DD and interval options), optional parameters (geo, plans, filters), and provides detailed guidance on filters (CFL syntax, referencing get_cfl_fields for field list). This adds significant meaning beyond the basic schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool retrieves Net MRR Churn Rate metrics as a percentage, clearly distinguishing it from sibling tools like 'mrr_metrics' or 'customer_churn_rate_metrics' by focusing specifically on MRR churn calculations. It provides the exact formula (Net MRR Churn = (Churned MRR + Contraction MRR - Expansion MRR) / MRR at Start × 100), making the purpose highly specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by specifying required parameters (start_date, end_date, interval) and mentioning optional filters, but it does not explicitly state when to use this tool versus alternatives like 'mrr_metrics' or 'customer_churn_rate_metrics'. It provides context on interpreting results (e.g., negative values indicate net negative churn) but lacks direct guidance on tool selection among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and adds significant behavioral context: it discloses that MRR values are in integer cents (requiring division by 100), explains the structure of the returned entries array and summary object, details MRR components (e.g., contraction as negative values), and provides an example. However, it does not mention rate limits, authentication needs, or error handling, leaving some 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 appropriately sized and front-loaded with critical information (e.g., integer cents warning). It uses bullet-like explanations efficiently, but could be slightly more structured—some sentences are long and dense, though all content earns its place by adding necessary details for tool invocation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (6 parameters, 0% schema coverage, no output schema, no annotations), the description is largely complete: it explains input requirements, output structure, and key behaviors. However, it lacks details on error cases, pagination, or exact response schema, which could be helpful for an agent. The absence of an output schema means the description should ideally cover return values more thoroughly, which it does partially but not exhaustively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate fully. It adds extensive meaning beyond the schema: it specifies required vs. optional parameters, explains data formats (e.g., YYYY-MM-DD for dates, CFL syntax for filters), provides an example filter string, and clarifies the purpose of each parameter (e.g., 'geo' and 'plans' for filtering). This goes well beyond the basic schema titles.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'retrieve' and resource 'Monthly Recurring Revenue metrics', distinguishing it from siblings like 'arr_metrics' or 'arpa_metrics' by specifying MRR. It explicitly mentions the ChartMogul API context, making the purpose specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by detailing required parameters (start_date, end_date, interval) and optional filters, but does not explicitly state when to use this tool versus alternatives like 'all_metrics' or 'mrr_churn_rate_metrics'. It provides context on filtering capabilities but lacks direct sibling comparison or exclusion guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It effectively discloses key behavioral traits: it's a read operation ('Retrieve'), returns a complete account object with specific fields and formats, and includes an example response. However, it doesn't cover aspects like error handling, authentication needs, or rate limits, leaving some 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 front-loaded with the core purpose, followed by details on returns and an example. Every sentence adds value: identifying the API, specifying the action, detailing the response structure, and providing an illustrative example without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 0 parameters, no annotations, and no output schema, the description is largely complete for a simple retrieval tool. It covers the purpose, return values, and an example. However, it lacks information on error cases or system constraints, which could be useful for full contextual understanding.
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 input schema has 0 parameters with 100% coverage, so the baseline is 4. The description explicitly states 'No parameters required,' which adds clarity beyond the schema by confirming the absence of inputs, aligning with the high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Retrieve your ChartMogul account information') and resource ('account information'), distinguishing it from sibling tools like retrieve_customer or retrieve_contact. It provides concrete details about what is retrieved, making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when account information is needed, but does not explicitly state when to use this tool versus alternatives (e.g., other retrieve_* tools for different resources). It mentions 'No parameters required,' which provides some context, but lacks explicit guidance on scenarios or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by detailing the return structure (complete invoice object with nested arrays for line_items and transactions) and specifying required parameters. It lacks information on error handling, rate limits, or authentication needs, but provides substantial behavioral context beyond basic retrieval.
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 appropriately sized and front-loaded with the core purpose, followed by return details and parameter specifics. It could be slightly more concise by avoiding repetition (e.g., 'full' in both line_items and transactions), but every sentence adds value, and it's well-structured for clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description provides good completeness for a retrieval tool: it explains the action, return data structure, and parameters. It lacks details on error cases or API limits, but for a tool with 2 parameters and no complex behavior, it's largely sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate fully. It adds significant meaning by explaining 'uuid' as 'invoice UUID' and 'validation_type' with its allowed values ('valid', 'invalid', or 'all') and purpose ('control included invoices'), which are not evident from the schema alone. This fully documents both parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Retrieve specific invoice by UUID') and resource ('invoice'), distinguishing it from sibling tools like 'list_invoices' (which returns multiple) and 'import_invoices' (which creates). It explicitly mentions the ChartMogul API context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by specifying it retrieves a 'specific' invoice, suggesting it should be used when you have the UUID, but does not explicitly state when to use it versus alternatives like 'list_invoices' or provide exclusions. It mentions the 'validation_type' parameter but not its impact on tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes critical behavioral traits: it discloses that monetary values are in integer cents (requiring division by 100), details the return structure (entries array with specific fields and a summary object), and provides syntax examples for filters. However, it doesn't mention rate limits, authentication needs, 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.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the purpose and critical monetary value warning, but it becomes lengthy due to detailed parameter explanations and examples. While informative, some parts (like the full list of return fields) could be more streamlined. Every sentence adds value, but the structure could be more concise for easier parsing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (6 parameters, no annotations, no output schema), the description is largely complete. It covers the purpose, parameters, return format, and critical behavioral notes. However, it lacks information on error cases, pagination, or authentication requirements, which would be helpful for a tool with multiple parameters and no structured output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must fully compensate. It adds substantial meaning beyond the bare schema: it explains that start_date, end_date, and interval are required with specific formats (YYYY-MM-DD and allowed values like 'day', 'week', etc.), clarifies that geo uses ISO 3166-1 Alpha-2 codes, details how to format plans and filters with syntax examples, and provides an example for monetary values. This goes well beyond the schema's basic titles.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Retrieve all key metrics for time period.' It specifies the exact resource (key metrics) and verb (retrieve), and distinguishes itself from sibling tools like arpa_metrics, arr_metrics, etc., which retrieve individual metrics rather than all metrics together.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: to retrieve all key metrics for a time period. It mentions the 'get_cfl_fields tool for complete field list' as a related tool, but doesn't explicitly state when to use this versus individual metric tools or other alternatives, nor does it provide exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the action ('Create'), mentions the return value ('Returns created note object'), and specifies field constraints (e.g., 'type' values, 'call_duration' for calls). However, it lacks details on permissions, error handling, or rate limits, which are important for a creation tool.
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 front-loaded with the core purpose, followed by a structured list of fields and their semantics, and ends with the return value. Every sentence adds value without redundancy, making it efficient and well-organized for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (creation tool with nested parameters), no annotations, and no output schema, the description does well by explaining parameters and the return value. However, it could improve by addressing behavioral aspects like authentication or error cases, which are missing but not critical for basic usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage and only one parameter ('data') as an object with no properties documented. The description compensates fully by detailing all nested fields (e.g., 'customer_uuid', 'type', 'author_email'), their types, constraints, and optionality, adding significant meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Create customer note/call log'), identifies the resource ('customer note'), and distinguishes it from siblings like 'list_customer_notes' or 'update_customer_note' by specifying creation rather than retrieval or modification. It also mentions the API context ('ChartMogul API'), adding specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage by listing required and optional fields, such as 'customer_uuid' and 'type', which implies when to use this tool for note creation. However, it does not explicitly state when not to use it or name alternatives like 'update_customer_note' for modifications, keeping it from a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the return format ('complete plan object with uuid, data_source_uuid, name, interval_count, interval_unit, external_id'), which is helpful behavioral context. However, it lacks details on error handling, authentication needs, or rate limits, which are important for a retrieval tool.
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 appropriately sized and front-loaded, with the core purpose in the first sentence and essential details (required parameter and format) following efficiently. Every sentence adds value without redundancy, 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.
Completeness4/5
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 provides good context by specifying the return object fields, which compensates for the lack of output schema. However, it could be more complete by mentioning potential errors or authentication requirements, though it's sufficient for a simple retrieval tool with one parameter.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It adds significant meaning beyond the schema by specifying that 'uuid' is a 'plan UUID with pl_ prefix' and provides an example format ('pl_eed05d54-75b4-431b-adb2-eb6b9e543206'), which clarifies the parameter's semantics and constraints not in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Retrieve specific plan by UUID') and resource ('plan'), distinguishing it from siblings like 'list_plans' (which retrieves multiple plans) and 'create_plan' (which creates rather than retrieves). The inclusion of 'ChartMogul API' provides context, and the verb 'retrieve' is precise.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by specifying 'specific plan by UUID', suggesting it should be used when you have a plan's UUID, as opposed to listing plans. However, it does not explicitly state when not to use it (e.g., vs. 'list_plans' for multiple plans) or name alternatives, keeping it from a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: idempotency ('no duplicates created'), mutation behavior ('New tags added to existing ones'), and return format ('Returns updated tags object'). It doesn't mention authentication needs, rate limits, or error conditions, but covers core operational behavior adequately.
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 efficiently structured with API context upfront, key behavioral notes, parameter requirements, and examples in a single dense paragraph. Every sentence adds value, though it could benefit from slightly better visual separation of information points.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 2-parameter mutation tool with no annotations and no output schema, the description provides good coverage: purpose, behavior, parameters, and return indication. It lacks details about error cases, authentication, or the exact structure of the 'updated tags object', but given the tool's relative simplicity, it's reasonably complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by explaining both parameters: 'uuid (string: customer UUID)' clarifies the identifier format, and 'tags (array: strings like ["vip", "priority"])' provides format examples and content expectations. This adds substantial meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Add tags to customer'), identifies the resource ('customer'), and distinguishes it from sibling tools like 'update_customer' or 'create_customer' by focusing exclusively on tag management. It provides a complete verb+resource+scope statement.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by specifying 'idempotent - no duplicates created' and 'New tags added to existing ones', which helps differentiate from potential alternatives. However, it doesn't explicitly name when-not-to-use scenarios or direct alternatives among the many sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing critical behavioral traits: ASP values are in integer cents requiring division by 100, the specific return format (entries array with date, asp, asp_percentage_change plus summary object), and interval constraints (day/week NOT supported). However, it doesn't mention rate limits, authentication requirements, or error conditions.
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 appropriately sized and front-loaded with the most critical information (ASP integer cents warning) first. Every sentence earns its place, though the example at the end could be slightly more integrated. Overall efficient with zero wasted text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 6 parameters with 0% schema coverage and no output schema, the description provides excellent coverage of inputs, return format, and critical constraints. It's nearly complete for this complexity level, though could benefit from mentioning authentication or error handling. The absence of an output schema makes the return format explanation particularly valuable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by explaining all 6 parameters: clearly marking 3 as REQUIRED with format details (YYYY-MM-DD dates, specific interval values), listing 3 OPTIONAL parameters, providing syntax examples for filters (CFL syntax), and referencing 'get_cfl_fields' for field lists. This adds substantial meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool retrieves Average Sale Price metrics from ChartMogul API, specifying ASP = Average first invoice amount of new customers. It clearly distinguishes from sibling tools like 'all_metrics', 'arpa_metrics', or 'arr_metrics' by focusing specifically on ASP calculations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about when to use this tool (for ASP metrics) and mentions an alternative tool ('get_cfl_fields' for filter field lists), but doesn't explicitly state when NOT to use it versus other metrics tools like 'mrr_metrics' or 'ltv_metrics'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes the return content ('CFL syntax rules, complete field-by-field reference with all operators and value formats, and practical examples'), which is helpful. However, it doesn't mention potential limitations like rate limits, authentication needs, or error conditions. For a read-only documentation tool with zero annotation coverage, this is adequate but not comprehensive.
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 well-structured and concise, with three sentences that each add value: the first states the purpose, the second provides usage guidelines, and the third details the return content. There is no wasted text, and information is front-loaded for clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (simple documentation retrieval with no parameters) and lack of annotations/output schema, the description is mostly complete. It covers purpose, usage, and return details. However, it could be more complete by mentioning any prerequisites (e.g., authentication) or behavioral traits like response format, though this is less critical for a zero-parameter tool.
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, and schema description coverage is 100% (though trivial since there are no parameters). The description doesn't need to add parameter semantics, so it meets the baseline of 4 for zero-parameter tools. It appropriately focuses on the tool's output rather than inputs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get comprehensive list of ChartMogul Filtering Language (CFL) fields and operators.' It specifies the verb ('Get'), resource ('CFL fields and operators'), and scope ('complete documentation for filtering metrics endpoints'), distinguishing it from sibling tools that focus on customer data, metrics, or CRUD operations rather than documentation retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool: 'Use this tool when you need to know what fields are available for the filters parameter in metrics endpoints, their types, supported operators, and examples.' It provides clear context for usage (filtering metrics endpoints) and implies an alternative approach (not using it when filtering knowledge is already known), though it doesn't name specific sibling alternatives since this is a unique documentation tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does an excellent job describing key behavioral traits: the default limit warning, pagination behavior ('Response includes cursor/has_more'), monetary value interpretation (INTEGER CENTS requiring division by 100), and the specific activity types available. The only minor gap is lack of explicit rate limit or authentication requirements.
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 appropriately sized and front-loaded with the core purpose. Each sentence adds value: purpose, limit warning, return format details, monetary value explanation, and parameter semantics. While comprehensive, it maintains good structure with clear sections marked by keywords like 'CRITICAL', 'Example', and 'FILTERS'.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 5 parameters, 0% schema coverage, no annotations, and no output schema, the description provides excellent coverage of what the tool does, how to use it, parameter meanings, and return format details. The only minor gap is the lack of explicit error handling or authentication context, but given the comprehensive parameter and behavioral coverage, this is quite complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by providing rich semantic information for all parameters. It explains the date filters (ISO 8601 format), type parameter values ('new_biz', 'reactivation', etc.), order parameter options ('-date' for descending, 'date' for ascending), and the limit parameter's default and warning. This goes well beyond what the bare schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('List customer activities') and resource ('across all customers'), with explicit scope ('revenue movements: new subscriptions, upgrades, downgrades, churn'). It distinguishes from siblings like 'list_customer_activities' by specifying it covers all customers rather than per-customer activities.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool (listing revenue activities across all customers) and includes a critical usage warning about the default limit of 20 items to avoid excessive token usage. However, it doesn't explicitly state when not to use it or name specific alternatives among the many sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does so effectively. It discloses key behavioral traits: the default limit of 20 items, a warning to avoid excessive token usage, the response structure (contact objects with detailed fields), pagination behavior (cursor/has_more), and filter examples. It does not cover aspects like rate limits or authentication needs, but provides substantial operational context.
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 appropriately sized and front-loaded, starting with the core purpose and immediately following with critical warnings and details. Every sentence earns its place, though the structure could be slightly more organized (e.g., separating warnings from response details).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (list operation with filtering and pagination), no annotations, and no output schema, the description is largely complete. It covers purpose, parameters, behavior, and response format. However, it lacks explicit error handling or authentication context, which could be beneficial.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate, which it does excellently. It explains all three parameters: 'email' and 'customer_external_id' as filters with examples, and 'limit' with its default value and a usage warning. This adds crucial meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('List') and resource ('contacts'), explicitly noting they are 'individuals associated with customers'. It distinguishes from siblings like 'retrieve_contact' (singular) and 'list_customers' (different resource).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage by mentioning filtering capabilities (email, customer_external_id) and pagination (cursor/has_more). However, it does not explicitly state when to use this tool versus alternatives like 'search_customers' or 'retrieve_contact', nor does it mention any exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does well by disclosing key behavioral traits: it warns about a default limit of 20 and discourages excessive requests to avoid token usage, describes the return format (subscription event objects with details), and mentions pagination (cursor/has_more). However, it lacks details on error handling or authentication needs.
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 appropriately sized and front-loaded with the core purpose, followed by important warnings and details. Every sentence adds value, but the long enum list for 'event_type' could be slightly condensed, though it's necessary for clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 9 parameters, 0% schema coverage, and no output schema, the description is quite complete: it covers purpose, usage constraints, parameter semantics, and response format. However, it lacks information on error cases or example responses, which would enhance completeness given the complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Given 0% schema description coverage, the description compensates fully by listing all 9 parameters with semantic details: it explains filter purposes (e.g., 'data_source_uuid' for filtering by data source), provides enum values for 'event_type', specifies date formats (ISO 8601), and clarifies the 'limit' parameter's default and warning. This adds significant meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('List subscription lifecycle events') and resource ('subscription events'), distinguishing it from sibling tools like 'list_customers' or 'list_invoices' by focusing on subscription-specific events. It also mentions tracking subscription changes, which further clarifies the purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage by specifying filters and a limit warning, but it does not explicitly state when to use this tool versus alternatives like 'list_customer_subscriptions' or 'update_subscription_event'. It implies usage for filtered event listing without naming specific alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does so effectively. It discloses critical behavioral traits: default limit (20), warning against excessive requests for token usage, detailed return object structure, and monetary value handling (INTEGER CENTS). This covers key operational aspects beyond basic functionality.
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 appropriately sized and front-loaded with the core purpose. However, the detailed return field list is lengthy and could be more streamlined; every sentence earns its place but the structure is slightly dense.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (2 parameters, no annotations, no output schema), the description is highly complete. It covers purpose, usage, behavioral details (limits, warnings), parameter semantics, and full return value explanation, compensating for all gaps in structured data.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, so the description must compensate fully. It adds significant meaning: 'uuid' is explained as a required customer UUID string, and 'limit' is contextualized with default (20) and a warning against high values to avoid token usage. This provides essential semantics not in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('List customer activities') and resource ('subscription lifecycle events'), distinguishing it from siblings like 'list_activities' (general activities) and 'list_customer_subscriptions' (different resource). It explicitly identifies the API source (ChartMogul API), adding precision.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage by specifying it's for customer activities and listing required parameters (uuid). However, it does not explicitly state when to use this tool versus alternatives like 'list_ustomer_subscriptions' or 'list_activities', missing explicit sibling differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does well by disclosing key behavioral traits: the default limit (20), pagination behavior ('Response includes cursor/has_more'), and the detailed return structure. It also mentions filtering capabilities and the types of line items included. The only minor gap is lack of explicit rate limit or authentication requirements.
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 appropriately sized and front-loaded with the core purpose and critical warning. The detailed return structure is necessary given no output schema, but the paragraph could be slightly more structured (e.g., bullet points for return fields). Every sentence adds value, with no redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (5 parameters, 0% schema coverage, no output schema, no annotations), the description is remarkably complete. It covers purpose, usage guidance, parameters, return structure, pagination behavior, and filtering options. The only minor omission is explicit error handling or authentication requirements, but overall it provides everything needed for effective tool use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by explaining all 5 parameters: it explicitly lists the four filter parameters (data_source_uuid, external_id, customer_uuid, validation_type) and provides context about the limit parameter with its default and warning. It also explains what values validation_type accepts ('valid', 'invalid', 'all').
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('List invoices') and specifies the resource scope ('contain line items generating subscription revenue'), distinguishing it from sibling tools like 'retrieve_invoice' (singular) and 'import_invoices' (write operation). It provides specific context about the API source (ChartMogul API).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear usage context with the 'LIMIT WARNING' advising against requesting more than 20 items to avoid excessive token usage, which implicitly suggests when to use this tool (for browsing/listing) versus alternatives like 'retrieve_invoice' for single-item lookups. However, it doesn't explicitly name alternatives or state when-not-to-use scenarios beyond the limit warning.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well. It discloses the return format (array of data source objects with specific fields), mentions the exact match nature of filters, notes case-sensitivity for system values, and provides example filter syntax. It doesn't cover rate limits, pagination, or error conditions, but gives substantial behavioral context beyond basic purpose.
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 efficiently structured: purpose statement, return format, filter explanations, usage guidance, and examples in a compact paragraph. Every sentence adds value - no redundant information. It's appropriately sized for a list/filter tool with 2 parameters.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a list/filter tool with 2 parameters and no output schema, the description is quite complete. It covers purpose, return format, parameter semantics, and usage guidance. The main gap is lack of information about pagination, rate limits, or error handling, but given the tool's relative simplicity and the detailed parameter coverage, it's mostly sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by explaining both parameters thoroughly. It defines 'name' as exact match filtering, 'system' as billing system type with examples (Stripe, Recurly, Custom) and notes case-sensitivity. It provides example syntax and clarifies these are optional filters (since no required parameters). This adds significant meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: listing data sources (billing systems connected to ChartMogul). It specifies the verb 'List' and resource 'data sources', distinguishes from siblings by focusing on data sources rather than customers, plans, or other entities, and provides concrete examples of what data sources are (Stripe, Recurly, Custom API).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: to list data sources and filter them by name or system. It mentions using results to filter other endpoints, which is helpful guidance. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the many sibling tools, though the context implies it's for data sources specifically.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well: it discloses the API context ('ChartMogul API'), specifies the return format ('complete data source object' with enumerated fields), and indicates it's a read operation ('Retrieve'). It doesn't mention error conditions, authentication needs, or rate limits, but provides solid behavioral context for a retrieval tool.
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 efficiently structured: first sentence states purpose, second details return format, third specifies parameter requirement with example. Every sentence earns its place with no wasted words, and critical information (UUID requirement) is appropriately front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter retrieval tool with no annotations and no output schema, the description is quite complete: it covers purpose, parameter semantics, and return format. The main gap is lack of explicit error handling or authentication context, but it provides sufficient information for correct tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, so the description fully compensates: it explains the 'uuid' parameter is REQUIRED, provides the exact format ('string: data source UUID like "ds_fef05d54-47b4-431b-aed2-eb6b9e545430"'), and clarifies the 'ds_' prefix pattern. This adds substantial meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Retrieve specific data source') and resource ('by UUID'), distinguishing it from sibling tools like 'list_sources' (which lists multiple) and other 'retrieve_' tools that target different resources. It specifies the exact object being retrieved with the UUID parameter requirement.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: when you need a specific data source identified by its UUID. It distinguishes from 'list_sources' by implication (retrieve specific vs. list all). However, it doesn't explicitly state when NOT to use it or name alternatives beyond what's obvious from sibling tool names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and delivers excellent behavioral disclosure. It explains the default limit (20), warns about token usage, describes pagination behavior (cursor and has_more), details the return format comprehensively, and specifies filtering options with examples. This goes well beyond basic functionality.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is information-rich but somewhat dense and could be better structured. While all content is valuable, the single paragraph format with detailed return field listing makes it less front-loaded. The warning and filtering details are well-placed, but the extensive field enumeration could be more efficiently organized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 5 parameters, 0% schema coverage, no annotations, and no output schema, the description provides exceptional completeness. It covers purpose, usage guidance, behavioral details, parameter semantics, return format, pagination, and examples. Nothing essential appears missing for effective tool invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by explaining all 5 parameters. It details what each filter does (data_source_uuid, external_id, status, system), provides status enum values, specifies system is case-sensitive, and explains the limit parameter with default value and usage warning. This adds substantial meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'List customers with optional filtering.' It specifies the resource (customers) and action (list) while distinguishing it from siblings like 'search_customers' by focusing on listing with filters rather than searching. The mention of ChartMogul API context further clarifies scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage with the 'LIMIT WARNING' and filtering options, but doesn't explicitly state when to use this tool versus alternatives like 'search_customers' or 'retrieve_customer'. It mentions filtering capabilities which helps guide usage, but lacks explicit sibling comparison.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and excels by disclosing key behavioral traits: it warns about a default limit of 20 and discourages higher requests to avoid excessive token usage, describes the return format (note objects with detailed fields), mentions pagination (cursor/has_more), and provides an example. This covers safety, output, and operational constraints effectively.
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 appropriately sized and front-loaded with purpose and key warnings. Every sentence adds value (e.g., limit warning, return fields, filters, pagination, example), but it could be slightly more structured (e.g., bullet points). No wasted text, though minor formatting improvements are possible.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 3 parameters, 0% schema coverage, no annotations, and no output schema, the description is highly complete: it covers purpose, usage, behavioral traits (limits, token usage, pagination), parameter semantics, and return format. This provides all necessary context for an agent to invoke the tool correctly without relying on structured fields.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate fully. It adds significant meaning beyond the schema: it explains that 'customer_uuid' and 'type' are filters (with 'type' values 'note' or 'call'), clarifies 'limit' has a default of 20 with a usage warning, and provides an example. This comprehensively documents all 3 parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('customer notes and call logs'), specifying it's for the ChartMogul API. It distinguishes from siblings like 'create_customer_note' (write vs. read) and 'retrieve_customer_note' (single vs. list), making the purpose specific and differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage with filters (customer_uuid, type) and a limit warning, but does not explicitly state when to use alternatives like 'list_customer_activities' or 'retrieve_customer_note'. It implies usage for filtered listing but lacks explicit exclusions or named alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and excels by disclosing critical behavioral traits: it specifies the default limit (20) and discourages higher values, details the response structure (subscription objects with specific fields), explains data interpretation (mrr/arr as integer cents), and mentions pagination (cursor/has_more). This covers safety, output format, and operational constraints effectively.
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 appropriately sized and front-loaded, starting with the core purpose and immediately detailing key information like the default limit and response structure. However, it could be slightly more structured by separating usage notes from field descriptions, but every sentence adds value without waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (2 parameters, no annotations, no output schema), the description is complete enough. It covers the tool's purpose, usage guidelines, behavioral details (like pagination and data interpretation), and parameter semantics, providing all necessary context for an AI agent to invoke the tool correctly without needing additional structured data.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, so the description must compensate fully. It adds significant meaning beyond the schema by explaining that 'uuid' is a required customer UUID and 'limit' has a default of 20 with a discouragement for values over 20. This clarifies the purpose and constraints of both parameters, making them understandable without relying on the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('List customer subscriptions') and resource ('customer subscriptions'), distinguishing it from sibling tools like 'list_customers' or 'list_subscription_events' by focusing on subscriptions for a specific customer. It provides concrete details about what is returned, making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context for usage by specifying that it requires a customer UUID and discourages limits over 20, but it does not explicitly mention when to use this tool versus alternatives like 'list_subscription_events' or 'retrieve_customer' for subscription-related data. It provides practical constraints but lacks sibling differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and delivers excellent behavioral disclosure. It reveals critical implementation details: default limit of 20, token usage concerns, exact return structure with 16 field descriptions, data format warnings (amount_in_cents as INTEGER CENTS), and available filter parameters. This goes far beyond basic functionality.
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 efficiently structured with clear sections (warning, returns, critical note, filters) and every sentence adds value. It's slightly long but justified by the comprehensive information needed for a 7-parameter tool with no annotations or output schema. Minor deduction for some redundancy in field explanations.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (7 parameters, 0% schema coverage, no annotations, no output schema), the description is remarkably complete. It covers purpose, behavioral constraints, detailed return format, parameter semantics, and practical warnings. For a list operation with filtering capabilities, this provides everything an agent needs.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage for 7 parameters, the description fully compensates by explicitly listing all filter parameters (customer_uuid, owner, pipeline, pipeline_stage, estimated_close_date_on_or_after, estimated_close_date_on_or_before) and mentioning the limit parameter with its default value. It provides meaningful context about what each filter does.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('sales opportunities') with specific context ('ChartMogul API', 'CRM feature'). It distinguishes from siblings like 'retrieve_opportunity' (singular retrieval) and 'create_opportunity' (creation).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about when to use it ('List sales opportunities') and includes a LIMIT WARNING with practical guidance ('Discourage requesting more than 20 items'). However, it doesn't explicitly mention when NOT to use it or name specific alternatives among the many sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
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 effectively describes key behavioral traits: it returns a complete customer object with nested fields, specifies critical data format details (e.g., mrr/arr are integer cents), and outlines required input format (UUID with cus_ prefix). This goes beyond basic functionality to include important usage constraints.
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 appropriately sized and front-loaded with the core purpose. Every sentence adds value, though it could be slightly more structured (e.g., separating input requirements from output details). No wasted words, but minor improvements in organization are possible.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (retrieval with nested data), no annotations, and no output schema, the description provides complete context. It explains what the tool returns (complete customer object with specific nested fields), critical data semantics (mrr/arr as integer cents), and precise input requirements, making it fully sufficient for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must fully compensate. It adds substantial meaning beyond the minimal input schema by detailing the uuid parameter's required format (string with 'cus_' prefix and UUID pattern like 'cus_de305d54-75b4-431b-adb2-eb6b9e546012'), which is crucial for correct tool invocation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Retrieve specific customer by UUID') and resource ('customer'), distinguishing it from sibling tools like list_customers (which lists multiple) and search_customers (which searches). It explicitly mentions the API context (ChartMogul API) for additional clarity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool (to retrieve a specific customer by UUID) and implies an alternative (list_customers for listing all customers). However, it does not explicitly state when not to use it or compare it to other retrieval tools like retrieve_contact or retrieve_account.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
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 effectively describes key behavioral traits: the default limit (20), a warning about excessive token usage, the return structure (same as list_customers), critical data format details (mrr/arr in integer cents), and exact match requirements for the email parameter. This covers mutation safety, performance considerations, and output expectations comprehensively.
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 appropriately sized and front-loaded with the core purpose. Every sentence adds value: purpose, limit warning, return structure, data format, and parameter details. However, it could be slightly more structured (e.g., bullet points) for easier parsing, and the API context '[ChartMogul API]' is redundant if implied by the server.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is complete. It covers purpose, usage guidelines, behavioral traits (limits, token usage, data format), and parameter semantics. No output schema exists, so the description appropriately explains the return structure and data format, leaving no significant gaps for agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate. It adds substantial meaning beyond the basic schema: it specifies that the email parameter requires an exact match (e.g., '[email protected]'), explains the limit parameter's default (20) and a warning against high values, and clarifies data semantics (mrr/arr as integer cents). This fully documents both parameters' usage and constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search customers by email address.' It specifies the exact resource (customers) and verb (search) with a precise search criterion (email address), distinguishing it from sibling tools like list_customers (which lists all customers) and retrieve_customer (which fetches a specific customer by ID).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: searching by email address. It implies an alternative (list_customers) by mentioning the return structure is the same, but does not explicitly state when to choose one over the other or list other alternatives like retrieve_customer. It also includes usage warnings (limit, token usage) but lacks explicit exclusions or prerequisites.
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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