Emailens MCP

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the agent knows this is a safe, non-destructive, repeatable operation. The description adds useful context about what the tool returns ('warnings and per-client scores') and its scope ('quick CSS compatibility analysis'), which goes beyond the annotations. However, it doesn't describe rate limits, authentication needs, or detailed behavioral traits beyond the basic operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise (two sentences) with zero wasted words. The first sentence states the purpose and output, the second provides crucial usage guidance. Every sentence earns its place, and the information is front-loaded with the core functionality stated immediately.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (CSS analysis with client-specific scoring), good annotations coverage, and 100% schema description, the description is mostly complete. It explains what the tool does, what it returns, and when to use it versus alternatives. The main gap is lack of output schema, so return values aren't formally documented, but the description does mention 'warnings and per-client scores' which helps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema description coverage, the input schema already fully documents both parameters (html and format with enum values). The description doesn't add any parameter-specific information beyond what's in the schema. The baseline score of 3 is appropriate when the schema does all the parameter documentation work.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

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 specific verbs ('analyze', 'returns') and resources ('CSS compatibility', 'warnings and per-client scores'). It explicitly distinguishes from its sibling 'audit_email' by contrasting 'quick CSS compatibility analysis' versus 'full quality report (spam, links, a11y, images, etc.)', making the distinction clear and specific.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool versus alternatives: 'Use audit_email for full quality report (spam, links, a11y, images, etc.)'. This clearly indicates that analyze_email is for quick CSS-focused analysis while audit_email is for comprehensive quality checks, giving the agent clear decision criteria between sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=false, covering safety and idempotency. The description adds context about what the audit entails (e.g., checks for spam, accessibility, size) and the skip functionality, but does not disclose additional behavioral traits like rate limits, auth needs, or output format. With annotations present, the bar is lower, and this adds moderate value.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose ('Comprehensive email quality audit') followed by a list of checks and usage note about 'skip,' all in two concise sentences. Every sentence earns its place by providing essential information without waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (auditing multiple email aspects) and rich annotations, the description is mostly complete, covering what the tool does and how to customize checks. However, there is no output schema, and the description does not explain return values or potential errors, leaving a minor gap. For a read-only tool with good annotations, this is acceptable but not perfect.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters (html, format, skip) thoroughly. The description mentions 'skip to omit specific checks,' which aligns with the schema but does not add significant meaning beyond it. With high schema coverage, the baseline is 3, and the description does not compensate with extra param details.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool performs a 'comprehensive email quality audit' and lists specific checks (CSS compatibility, spam scoring, link validation, etc.), distinguishing it from sibling tools like analyze_email, fix_email, or preview_email which likely have different scopes. The verb 'audit' with the resource 'email' is specific and comprehensive.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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 (for email quality audits covering multiple aspects) and mentions the 'skip' parameter to omit specific checks, implying flexibility. However, it does not explicitly state when not to use it or name alternatives among sibling tools (e.g., vs. analyze_email or check_deliverability), which would be needed for a score of 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds valuable behavioral context beyond annotations: it discloses the API key requirement (auth needs), mentions the free plan availability (cost/rate limit context), and describes the rendering method ('rendered in real browsers') and hosting location ('hosted on CDN'). These details aren't covered by the existing annotations, which only provide basic hints about mutability and idempotency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured in two sentences: the first explains the core functionality and scope, the second covers implementation details and requirements. Every element serves a purpose with zero wasted words, making it easy to parse while being information-dense.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 5 parameters, no output schema, and annotations covering basic behavioral hints, the description provides good contextual completeness. It explains the rendering method, hosting, client support, and authentication requirements. The main gap is lack of information about return values or error handling, but given the annotations and parameter coverage, it's reasonably complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema description coverage, the schema already documents all 5 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline expectation but doesn't provide additional semantic context about how parameters interact or affect behavior.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('capture real email screenshots'), resources involved ('across 15 clients'), and scope ('with light and dark mode variants'). It distinguishes itself from siblings like 'preview_email' or 'analyze_email' by emphasizing screenshot capture rather than analysis or previewing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context by mentioning 'real email screenshots' and 'hosted on CDN', but doesn't explicitly state when to use this tool versus alternatives like 'preview_email' or 'share_preview'. It provides some prerequisites (API key requirement) but lacks explicit when/when-not guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds valuable behavioral context beyond the annotations: it specifies that the tool performs DNS lookups, requires no API key, and returns a score (0-100) with actionable issues. While annotations already indicate it's read-only, non-destructive, idempotent, and open-world, the description enhances understanding of the tool's operational characteristics and output format.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is highly concise and well-structured in a single sentence that front-loads the core purpose, lists key features, and ends with implementation details. Every element ('Returns a score...', 'Uses DNS lookups...') adds essential information without redundancy or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (DNS-based analysis), rich annotations, and full schema coverage, the description is largely complete. It explains what the tool does, how it operates, and what it returns. The main gap is the lack of an output schema, but the description compensates by specifying the return format (score 0-100 with actionable issues).

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema description coverage, the input schema already fully documents the single 'domain' parameter. The description doesn't add any parameter-specific details beyond what's in the schema, so it meets the baseline of 3 for adequate but not enhanced parameter semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Check email deliverability for a domain') and enumerates the exact resources examined (SPF, DKIM, DMARC, MX, and BIMI records). It distinguishes from siblings like 'analyze_email' or 'audit_email' by focusing specifically on DNS-based domain deliverability rather than email content analysis or broader audits.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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 ('Check email deliverability for a domain') and mentions it 'Uses DNS lookups (no API key needed)', which helps differentiate it from tools that might require authentication. However, it doesn't explicitly state when NOT to use it or name specific alternative tools among the siblings for different scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the agent knows this is a safe, non-destructive operation. The description adds useful context about what the tool outputs ('score changes, fixed issues, and newly introduced issues per client'), which goes beyond the annotations but doesn't cover rate limits, authentication needs, or 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is perfectly concise with two sentences that are front-loaded and waste no words. Every phrase ('Compare two email HTML versions', 'shows score changes...', 'Use after making fixes...') directly contributes to understanding the tool's purpose and usage.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity, rich annotations, and 100% schema coverage, the description is mostly complete. It explains the tool's purpose, output types, and usage context well. However, without an output schema, it could benefit from more detail on return format or error handling, though this is partially mitigated by the clear behavioral context provided.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all three parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema, maintaining the baseline score for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

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 specific verbs ('compare', 'shows') and resources ('two email HTML versions'), distinguishing it from siblings like analyze_email or audit_email by focusing on comparison rather than standalone analysis.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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 the tool ('Use after making fixes to verify improvements'), which helps differentiate it from siblings like fix_email or preview_email. However, it doesn't explicitly state when not to use it or name specific alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds valuable context beyond annotations: it specifies the output format ('Returns markdown with the original code, detected issues, fix snippets, and format-specific instructions'), which isn't covered by annotations. Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=false, so the description doesn't contradict them and enhances understanding of the tool's behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose, followed by output details and usage guidelines in just two sentences, with zero wasted words—every sentence earns its place by adding essential information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (4 parameters, no output schema), the description is mostly complete: it covers purpose, output format, and usage context. However, it lacks details on error handling or specific behavioral constraints (e.g., rate limits), 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.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema description coverage, the schema fully documents all parameters. The description adds minimal param semantics by mentioning 'format-specific instructions' (hinting at the format parameter's role) but doesn't provide significant additional meaning beyond what the schema already specifies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

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 specific verbs ('Generate a structured fix prompt') and resources ('for email compatibility issues'), distinguishing it from siblings like analyze_email or preview_email by focusing on generating fixes rather than analysis or previewing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly states when to use this tool ('Use after preview_email or analyze_email'), providing clear context and distinguishing it from alternatives by positioning it as a follow-up step to those specific tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, covering safety and idempotency. The description adds valuable context beyond annotations by specifying the exact count (15 clients) and data fields returned, which helps set expectations about the fixed dataset (openWorldHint=false supports this). No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence efficiently conveys purpose, scope, and return data. Every word earns its place with no redundancy or fluff. Front-loaded with the core action ('List all 15 supported email clients').

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple read-only listing tool with rich annotations (safety, idempotency, closed world) and no parameters, the description is nearly complete. It specifies the dataset size and fields, though lacks output format details (e.g., structure, pagination). Since there's no output schema, this minor gap prevents a perfect score.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0 parameters and 100% schema description coverage, the baseline is high. The description appropriately states there are no inputs needed, implicitly confirming the empty parameter set. It adds value by clarifying what data is returned, though this overlaps somewhat with what an output schema would provide.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('List') and resource ('all 15 supported email clients'), specifying exactly what data is returned (IDs, names, engines, dark mode support). It distinguishes from siblings like 'analyze_email' or 'check_deliverability' by focusing on metadata listing rather than email analysis or testing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when needing client metadata, but provides no explicit guidance on when to use this tool versus alternatives like 'audit_email' or 'preview_email'. There's no mention of prerequisites, exclusions, or specific scenarios where this tool is preferred over others.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate read-only, non-destructive, and idempotent behavior, so the description adds value by detailing specific capabilities beyond safety: it lists the 15 email clients supported, mentions analysis of CSS, scoring, dark mode simulation, and checks for inbox preview and size. This provides useful context about the tool's comprehensive preview features, though it doesn't cover rate limits or auth needs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose and efficiently lists key features in a single, dense sentence without waste. Every element (e.g., client list, analysis types) earns its place by informing the tool's scope, 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.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity, rich annotations, and 100% schema coverage, the description is largely complete: it details the preview functionality, client support, and analysis features. However, without an output schema, it doesn't explain return values like scores or preview data, leaving a minor gap in full context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents parameters like html, clients, and format. The description adds no additional parameter semantics beyond what's in the schema, such as examples or constraints, but doesn't need to compensate given the high coverage. The baseline score of 3 reflects adequate but no extra value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('preview — transforms HTML for 15 email clients') and resource ('email compatibility'), distinguishing it from siblings like analyze_email or audit_email by focusing on client-specific rendering rather than general analysis or compliance checks. It explicitly lists the key email clients covered, making the scope concrete.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for email compatibility testing across clients, but does not explicitly state when to use this tool versus alternatives like analyze_email or capture_screenshots. It mentions specific features like dark mode simulation, which suggests use cases, but lacks direct guidance on tool selection or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate this is a non-readOnly, non-destructive, non-idempotent, openWorld operation. The description adds useful context about authentication requirements (EMAILENS_API_KEY) and cost (Dev plan $9/mo), which are not covered by annotations. However, it lacks details on rate limits, response format, or error handling, keeping the score at a moderate level.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose in the first sentence, followed by essential context in the second. Every sentence earns its place by providing critical information without redundancy, making it highly efficient and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (3 parameters, no output schema) and rich annotations, the description is mostly complete. It covers purpose, prerequisites, and key behavioral context, but it could improve by mentioning output details (e.g., what the shareable link looks like) or error cases, slightly reducing the score.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema description coverage, the input schema fully documents all parameters (html, title, format). The description does not add any semantic details beyond what the schema provides, such as examples or usage tips for the parameters. Thus, it meets the baseline but does not enhance understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Create a shareable link') and resource ('for an email preview'), distinguishing it from siblings like analyze_email, preview_email, or capture_screenshots. It specifies that recipients see 'full analysis without needing an account,' which further clarifies the tool's unique function.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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 share email previews externally) and mentions prerequisites (EMAILENS_API_KEY env var and Dev plan), but it does not explicitly state when not to use it or name alternatives among siblings. This omission prevents a perfect score.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.