revenuebase-mcp-server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: batch_email_submission initiates processing, batch_process_email_status checks status, cancel_process stops jobs, get_credits shows credits, new_api_key generates keys, queued_process lists jobs, and real_time_email_verification verifies single emails. The descriptions reinforce these distinct functions, making tool selection unambiguous.
Naming Consistency5/5
All tools follow a consistent snake_case naming pattern with clear verb-noun combinations (e.g., batch_email_submission, real_time_email_verification). The naming convention is uniform across all seven tools, making them predictable and easy to understand at a glance.
Tool Count5/5
With 7 tools, this server is well-scoped for email verification and batch processing tasks. Each tool serves a specific, necessary function in the workflow, from initiation to status checks and management, without being overly sparse or bloated.
Completeness4/5
The tool set covers core email verification and batch processing operations comprehensively, including submission, status retrieval, cancellation, credit management, and API key generation. A minor gap exists in lacking a tool for detailed results of completed batch jobs, but agents can work around this using the status tool.
Average 3/5 across 7 of 7 tools scored. Lowest: 2.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'submits' implying a write operation, but doesn't disclose behavioral traits like whether this is asynchronous, what permissions are required, potential rate limits, or what happens after submission (e.g., does it return a process ID?). The description is minimal and lacks critical 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 a single, efficient sentence with no wasted words. It's front-loaded with the core action and resource, though it could benefit from slightly more detail without sacrificing brevity. The structure is clear but minimal.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and low parameter coverage, the description is incomplete. It doesn't explain what the tool returns (e.g., success/failure, a process ID), error conditions, or how it integrates with sibling tools. For a submission tool with potential side effects, this leaves significant gaps in understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It only mentions 'file reference' vaguely, without explaining what 'filename' represents (e.g., a local file path, a URL, a pre-uploaded reference). No details on format, constraints, or examples are provided, leaving the parameter poorly defined beyond the schema's basic type.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the action ('submits') and resource ('file reference for batch email processing'), but lacks specificity about what the processing entails. It mentions the Revenuebase API, which provides some context, but doesn't clearly differentiate this tool from sibling tools like 'batch_process_email_status' or 'queued_process' that might handle related batch operations.
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. With siblings like 'batch_process_email_status', 'cancel_process', and 'queued_process', the description offers no indication of prerequisites, sequencing, or distinctions between submission and status-checking tools, leaving the agent to infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('verifies') but doesn't describe what verification entails (e.g., checks for validity, deliverability, or spam traps), whether it's synchronous or real-time, error handling, or any rate limits. This leaves significant gaps for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste: 'Verifies a single email address using the Revenuebase API.' It's front-loaded with the core action and appropriately sized for the tool's apparent simplicity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what the verification returns (e.g., status codes, confidence scores) or behavioral aspects like error cases. For a verification tool with no structured context, this leaves the agent under-informed about how to interpret results.
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 1 parameter with 0% description coverage, so the description must compensate. It mentions 'a single email address' but doesn't specify the format (e.g., valid email syntax) or constraints (e.g., domain requirements). This adds minimal semantic value beyond what's implied by the parameter name 'email'.
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: 'Verifies a single email address using the Revenuebase API.' It specifies the verb ('verifies'), resource ('a single email address'), and mechanism ('using the Revenuebase API'). However, it doesn't explicitly differentiate from sibling tools like batch_email_submission or batch_process_email_status, which likely handle multiple emails or status checks.
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 siblings like batch_email_submission for multiple emails or queued_process for asynchronous handling, nor does it specify prerequisites such as API key requirements or rate limits. Usage context is implied but not explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states this is a retrieval operation, implying read-only behavior, but doesn't cover aspects like error handling, rate limits, authentication needs, or what the status output includes (e.g., progress, errors). For a tool with no annotations, this leaves significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's front-loaded with the core action and resource, making it easy to scan and understand quickly without unnecessary details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and low schema coverage, the description is incomplete. It lacks details on behavioral traits, parameter usage, and expected return values (e.g., status format, possible states). For a tool that retrieves job status, more context is needed to guide effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It implies the 'process_id' parameter is needed to identify the job, adding some meaning beyond the schema's title ('Process Id'). However, it doesn't explain the parameter's format, source (e.g., from 'batch_email_submission'), or constraints, providing only basic 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 ('Retrieves') and resource ('status of batch email processing job'), making the purpose understandable. However, it doesn't distinguish this tool from potential sibling tools like 'queued_process' or 'cancel_process' that might also relate to batch processing, missing full differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., needing a process_id from a prior job), exclusions, or comparisons to siblings like 'queued_process' or 'batch_email_submission', leaving usage context unclear.
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 action ('Cancels') but doesn't describe what cancellation entails (e.g., irreversible, partial completion, notifications), error conditions (e.g., invalid ID, already completed), permissions required, or rate limits. This leaves significant 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It front-loads the key action ('Cancels') and resource, and the mention of 'Revenuebase API' provides useful context without redundancy. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (a mutation tool with no annotations, 0% schema coverage, and no output schema), the description is incomplete. It lacks details on behavior, error handling, prerequisites, and what to expect after cancellation. For a tool that performs a potentially destructive action, this is inadequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate. It implies a 'process_id' is needed by mentioning 'batch email processing job', but doesn't explain what a process_id is, where to get it, or its format. This adds minimal semantic value beyond the schema's basic structure, resulting in a baseline score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Cancels') and the target resource ('ongoing or queued batch email processing job'), which is specific and actionable. It distinguishes from siblings by focusing on cancellation rather than submission, status checking, or other operations. However, it doesn't explicitly differentiate from all siblings (e.g., 'queued_process' might be related).
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 a valid process_id), when cancellation is appropriate (e.g., for jobs in specific states), or what happens if used incorrectly. No explicit alternatives or exclusions are 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 the full burden of behavioral disclosure. It mentions that it 'Generates and returns' an API key, implying a write operation, but lacks details on permissions needed, rate limits, whether the key is immediately active, or if old keys are invalidated. This leaves significant gaps for a tool that likely involves security-sensitive actions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no wasted words, making it easy to parse and understand quickly. It's appropriately sized for a simple tool with no parameters.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of generating an API key (a security-sensitive write operation), no annotations, and no output schema, the description is incomplete. It doesn't explain what the returned API key looks like, any associated metadata, or behavioral traits like authentication requirements, making it inadequate for safe and effective use.
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 no parameter documentation is needed. The description doesn't add parameter details, which is appropriate here, but it could have mentioned if any implicit parameters (like user context) are involved. Baseline is 4 for 0 parameters.
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 ('Generates and returns') and the resource ('a new API key for the user'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools, as none appear to be related to API key management, so it doesn't need explicit distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, prerequisites, or any context about its role among the sibling tools. It simply states what it does without indicating appropriate scenarios 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 lists jobs, implying a read-only operation, but doesn't specify if it requires authentication, returns paginated results, or includes metadata like timestamps. This leaves gaps in understanding the tool's behavior beyond basic listing.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's function without any fluff or redundancy. It's front-loaded with the core action and resource, making it easy to parse quickly, which is ideal for a simple listing tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (0 parameters, no output schema, no annotations), the description is minimally adequate. It explains what the tool does but lacks details on return format (e.g., list structure, fields) and behavioral context like error handling. For a listing tool with no structured support, more completeness would enhance usability.
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 no parameter documentation is needed. The description adds no parameter details, which is appropriate here, as there's nothing to compensate for. A baseline of 4 is given since the schema fully covers the absence of parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Lists') and resource ('queued email batch processing jobs'), making it immediately understandable. However, it doesn't differentiate from sibling tools like 'batch_process_email_status' which might also relate to processing status, leaving room for potential confusion about when to use each.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'batch_process_email_status' or 'cancel_process'. It lacks context about prerequisites, such as whether jobs must be in a specific state, or exclusions, leaving the agent to infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 indicates this is a read operation ('Retrieves'), which is helpful, but doesn't mention potential rate limits, authentication requirements beyond 'authenticated user,' error conditions, or return format. The description adds basic context but lacks detailed behavioral traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose with zero wasted words. It immediately communicates what the tool does without unnecessary elaboration, making it easy to parse and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no output schema), the description is adequate but could be more complete. It explains the purpose clearly but lacks details on return values (e.g., numeric count, object structure), error handling, or dependencies. For a read-only tool with no parameters, this is minimally viable but leaves gaps in behavioral 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?
The tool has 0 parameters, and schema description coverage is 100% (though empty). The description appropriately doesn't discuss parameters since none exist, which is sufficient for this case. No additional parameter semantics are needed beyond what the schema already indicates.
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 ('Retrieves') and resource ('number of remaining credits for the authenticated user'), making the tool's purpose immediately understandable. It distinguishes itself from sibling tools which focus on email processing, API keys, and process management rather than credit retrieval.
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 by specifying 'for the authenticated user,' suggesting this tool should be used when checking credit balances. However, it provides no explicit guidance on when to use this versus alternatives (none of which appear to be credit-related), 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.

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