chargebee-mcp

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct resource and action: customer CRUD, customer contacts, subscription list/retrieve/cancel, invoice list, and transaction list. There is no overlap or ambiguity between tool purposes.
Naming Consistency5/5
All tools follow a consistent 'chargebee_verb_noun' pattern with snake_case (e.g., chargebee_list_customers, chargebee_retrieve_subscription). The naming is uniform and predictable across the entire set.
Tool Count5/5
With 10 tools, the server is well-scoped for a billing-focused API. Each tool covers a meaningful operation without excessive fragmentation or unnecessary bloat.
Completeness3/5
The tool set thoroughly covers customer management and read operations for subscriptions, invoices, and transactions. However, it lacks subscription creation/update and invoice-level actions, which are notable gaps for a billing server's lifecycle.
Average 4.1/5 across 10 of 10 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 6 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under Apache 2.0.
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.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing side effects. It does not state that cancellation is irreversible, affects billing, or requires specific permissions. The API endpoint and parameter details imply a write operation, but the actual consequences of cancelling a subscription are not explicitly described.
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 with a summary, API reference, and an args list. Every line adds value, including the legacy note for end_of_term. It is appropriately sized for a tool with 7 parameters and is front-loaded with the core purpose.
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?
The parameter documentation is thorough, and an output schema exists (so return values are covered elsewhere). However, the description lacks usage context (when to cancel vs retrieve) and behavioral consequences, which are critical for a mutating tool. It is complete enough for basic invocation but not for fully informed decision-making.
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 explain parameters. It provides detailed semantics for all 7 parameters, including enum-like values for cancel_option, credit_option_for_current_term_charges, and unbilled_charges_option, plus the relationship between cancel_at and cancel_option='specific_date'. This far exceeds minimal requirements.
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 'Cancel a subscription' with a specific verb and resource, and it distinguishes itself from sibling tools like retrieve/list by indicating a mutating action. The API endpoint further reinforces 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 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 (e.g., retrieve_subscription for reading). The description only states what it does, not the context in which cancellation is appropriate or any preconditions.
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 present, so the description must disclose behavioral details. It provides the API method and endpoint but does not explain whether the update is partial or full, idempotency, permission requirements, or potential side effects. This leaves significant behavioral ambiguity.
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 docstring-style layout is efficient for 11 parameters, starting with the core purpose and API endpoint. It avoids unnecessary fluff but is somewhat formulaic in repeating each argument on its own line; still appropriately sized.
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?
The description covers all parameters and the endpoint, but lacks behavioral context such as update semantics, usage guidelines, and any caveats. With an output schema present, return values are not needed, but the missing behavioral and usage guidance leaves the tool only partially 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?
Every parameter receives a concise semantic definition in the description, far exceeding the bare schema types. It adds concrete meaning such as allowed values for auto_collection and taxability, and the purpose of invoice_notes and meta_data, which the schema lacks entirely.
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 'Update a customer' and specifies the API endpoint, making the action unambiguous. It distinguishes from sibling tools like retrieve, list, and create by focusing on modification.
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?
Usage is implied from 'Update a customer,' but no explicit guidance is given on when to use this tool versus alternatives, nor are exclusions or prerequisites mentioned. The context of modifying an existing customer is understood but not spelled out.
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 must convey behavioral traits. It explicitly states this is a GET operation, signaling a safe read. It does not detail error cases, authentication, or return format, but for a simple retrieval the GET disclosure is a minimal yet adequate transparency step.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with zero wasted words. It includes the API endpoint, a single argument definition, and nothing else. Perfectly front-loaded and easy to scan.
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 simple single-parameter retrieve tool with an output schema, the description covers the essential purpose and parameter. It doesn't explain return values, but the output schema handles that. The API endpoint adds useful context. It would benefit from noting possible errors, but overall complete for its simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate. It describes customer_id as 'The customer's unique ID', which adds meaning beyond the schema's bare 'Customer Id' label. However, this is still minimal; it doesn't clarify formats, examples, or how to find the ID.
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 a customer by ID, using the specific verb 'Retrieve' and resource 'customer'. It distinguishes itself from siblings like list, create, and update customers.
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 clearly implies use when you have a customer ID and need that specific customer's details. It doesn't explicitly mention alternatives, but the singular 'by ID' naturally contrasts with listing or creating. The API endpoint reinforces the targeted retrieval context.
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 must convey behavior. It specifies the HTTP GET method, which strongly implies a read-only, non-destructive operation. It does not disclose error handling, response format, or access requirements, but the GET method plus 'retrieve' gives a safe profile.
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 concise and front-loaded with the main action, then includes only the API endpoint and a single parameter explanation. No unnecessary words or 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 tool's simplicity (one parameter, output schema exists), the description provides the essential purpose and parameter semantics. Return values are covered by the output schema, so no need to detail them. It lacks explicit usage exclusions, but that is a guidance issue handled in dimension 2.
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 no description for the subscription_id parameter (0% coverage), but the description compensates by explaining it as 'The subscription's unique ID.' This adds meaning beyond the schema's type and title.
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 'Retrieve a subscription (account) by ID' with a specific verb and resource. It distinguishes from siblings like list_subscriptions (list) and cancel_subscription (mutation) by focusing on retrieval of a single subscription.
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?
Usage is implied: use when you have a subscription ID and need its details. However, there is no explicit comparison with alternatives like list_subscriptions or retrieve_customer, nor guidance on when not to use this tool.
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 burden. It discloses the API endpoint, auto-generated id behavior, and allowed values for auto_collection/taxability, but does not mention permissions, error handling, or side effects beyond creation.
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?
Concise docstring with front-loaded purpose, API line, and one line per parameter. Every sentence adds value; no 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?
Covers all parameters and provides the API endpoint, but lacks usage guidance and behavioral details like auth or idempotency. Since an output schema exists, return values are not required, making it mostly complete for its 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?
Schema coverage is 0%, and the description compensates by defining all 11 parameters, including allowed values for auto_collection and taxability, an example for billing_address, and the optional/auto-generated behavior of id.
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?
Clearly states 'Create a customer (company)' with a specific verb and resource. The API endpoint 'POST /customers' reinforces the action, and the tool is distinct from sibling tools like retrieve/update/list.
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 does not explicitly state when to use this tool vs alternatives, but the verb 'Create' implies the use case for new customer creation. No exclusions or alternative recommendations 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?
No annotations are provided, so the description carries the full burden. It clearly indicates a read operation ('List') and includes the API endpoint (GET), which is helpful. However, it does not disclose potential issues like error handling, authentication requirements, or exact response structure beyond pagination, leaving some behavioral ambiguity.
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 concise and well-structured: a one-sentence purpose, the API endpoint, and a compact Args list. Every sentence adds value with no redundancy or fluff.
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 simple list tool with an output schema, the description is fairly complete: it covers purpose, API endpoint, and all parameters. It does not explain return values (unnecessary due to output schema), but lacks details on edge cases like invalid customer_id or empty results, which prevents a perfect score.
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 zero coverage in the schema, the description's Args section fully compensates by explaining each parameter: customer_id as 'unique ID', limit with range (1-100) and default, and offset as a 'pagination cursor from previous response's next_offset'. This adds significant meaning beyond the schema's types and defaults.
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 lists contacts (personnel) for a customer, using a specific verb and resource. This distinguishes it from sibling tools like chargebee_list_customers (which lists customers) and chargebee_retrieve_customer (which retrieves a single 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 purpose implicitly indicates when to use this tool (when you need contacts of a specific customer), but it does not explicitly mention alternatives or exclusions. There is no guidance on choosing between this and chargebee_retrieve_customer if the agent needs full customer details, so usage context is implied but not fully articulated.
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, the description carries the full burden. It details pagination via limit and offset, includes include_deleted behavior, and thoroughly explains the filter syntax including supported fields and operators. It does not mention rate limits or authorization, but for a read-only list operation, the disclosed behavior is substantial and non-contradictory.
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: a one-line purpose, the endpoint, then a bulleted Args list. The filter details are lengthy but necessary for correct usage. It is front-loaded with the core 'List invoices' statement. Minor redundancy with the schema (e.g., limit default) is acceptable given the low schema coverage.
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 complexity (filtering with many fields/operators), the description fully covers all parameters, pagination mechanics, and filter behavior. An output schema exists, so return-value details are not required. The description is sufficient for an agent to invoke the tool correctly with minimal missing 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 does. Each parameter (limit, offset, include_deleted, filters) is explicitly described with defaults, types, and even a JSON example for filters. The filter field and operator lists provide far more meaning than the bare schema properties, which only specify types and defaults.
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 opens with 'List invoices'—a specific verb and resource. It clearly distinguishes from sibling tools like list_subscriptions and list_transactions by naming invoices as the target. The API endpoint reinforces the purpose without ambiguity.
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 does not explicitly state when to use this tool over alternatives, nor does it mention exclusions or sibling tools. It implies usage through the resource name, but lacks direct guidance like 'Use this for invoice history' or 'For transactions, use chargebee_list_transactions.' This is a neutral, minimal usage signal.
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, the description fully discloses behavioral traits: pagination via limit and offset, include_deleted flag, and a thorough explanation of filters including supported fields and operators. It also mentions the API endpoint. This is a high level of transparency for a read operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-organized: a concise purpose line, the API endpoint, then each argument with clear formatting. The filter details are lengthy but necessary, and there is no redundancy. Every sentence contributes useful 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?
The tool has 4 optional parameters including a complex filter object, and no annotations. The description covers all invocation needs, including parameter semantics and behavior. An output schema exists, so return values are not required in the description. The only minor gap is usage guidance, already scored separately.
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 coverage is 0%, but the description adds substantial meaning to each parameter: limit's range and default, offset as a pagination cursor from next_offset, include_deleted's effect, and filters with examples and detailed supported fields/operators. This goes far beyond the basic schema types.
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 'List subscriptions (accounts).' with a specific verb and resource. It distinguishes from siblings like retrieve_subscription (singular) and cancel_subscription by indicating a listing operation for multiple subscriptions.
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 such as retrieve_subscription or cancel_subscription. It mentions no prerequisites, exclusions, or comparison with other tools, relying solely on the implied purpose.
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, the description carries the burden and discloses the API endpoint, pagination via offset and next_offset, the option to include deleted transactions, and detailed filter semantics. This goes beyond a simple 'list' and provides expectations around read-only behavior and pagination.
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 with a one-sentence summary, API endpoint, and a clear Args list. Every sentence provides necessary detail, especially for the complex filter syntax, without redundancy. It is appropriately front-loaded.
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?
Despite the complexity, the description covers all necessary aspects: pagination behavior, filtering fields and operators, inclusion of deleted records, and param defaults. An output schema exists, so return values are documented externally. The description fully supports invocation without missing crucial information.
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%, but the description comprehensively explains each parameter: limit with range and default, offset as a cursor, include_deleted as a boolean, and filters with supported field types and operators. The filters section adds substantial meaning beyond the schema's bare property definitions.
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 states 'List payment/refund transactions' with a specific verb and resource, clearly distinguishing it from sibling tools that list customers, subscriptions, or invoices. It also specifies the API endpoint, reinforcing the action.
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 a list of transactions is needed, but it does not explicitly provide guidance on when to use this tool versus alternatives like chargebee_list_invoices. No when-not-to-use or alternative tooling is mentioned.
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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It thoroughly explains pagination via offset, the include_deleted flag, and the complex filter syntax including supported fields and operators. This provides substantial behavioral transparency beyond the basic 'list' semantics, and it is clearly a read-only 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 structured and front-loaded with the core purpose, followed by parameter explanations. While lengthy, every section is necessary to document the flexible filtering and pagination. It could be slightly condensed, but it remains efficient.
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 complexity and the presence of an output schema, the description covers all necessary usage aspects: API endpoint, pagination, filtering fields and operators. It does not need to document return values because the output schema exists. The description is complete for an agent to invoke the tool correctly.
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 zero descriptions, so the description must compensate. It does so effectively by explaining limit's range and default, offset as a pagination cursor, include_deleted, and the entire filter structure with examples and supported operators. This adds significant meaning beyond the raw 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 'List customers (companies)' with the API endpoint GET /customers. This distinguishes it from sibling tools like retrieve/create/update customers, and specifies that customers are companies.
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: it is the list operation for customers, with pagination and filtering options. It does not explicitly mention alternatives or exclusions, but the sibling tools are distinct (retrieve, create, update), so the usage is evident.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![chargebee-mcp MCP server](https://glama.ai/mcp/servers/MSPbotsAI/chargebee-mcp/badges/card.svg)](https://glama.ai/mcp/servers/MSPbotsAI/chargebee-mcp)

Score Badge

Copy to your README.md:

[![chargebee-mcp MCP server](https://glama.ai/mcp/servers/MSPbotsAI/chargebee-mcp/badges/score.svg)](https://glama.ai/mcp/servers/MSPbotsAI/chargebee-mcp)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/MSPbotsAI/chargebee-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server