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de-Blanck

protonmail-mcp-server

by de-Blanck
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clear, distinct purpose: listing folders, listing emails, searching emails, and retrieving full email content. No overlap, even between list and search due to search's advanced filtering.

    Naming Consistency5/5

    All tools follow a consistent 'proton_verb_noun' snake_case pattern (list_folders, list_emails, search_emails, get_email). No deviation in style or convention.

    Tool Count4/5

    With 4 tools, the set is small but appropriate for a read-only email interface. It covers essential reading operations without unnecessary bloat, though some users might expect a few more common actions.

    Completeness3/5

    The tools cover the core read workflow (list folders, list/search emails, read full email) well, but completely lack write operations like send, delete, move, or mark as read. This is a notable gap for full email management.

  • Average 4.5/5 across 4 of 4 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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

  • Behavior4/5

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

    Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds behavioral details about returned data (message/unread counts) and the response format parameter, enhancing transparency.

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

    Conciseness4/5

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

    The description is concise and well-structured with a clear purpose sentence, Args, Returns, and Examples. It is front-loaded with the key action. Minor room for trimming.

    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 read-only list tool, the description covers purpose, usage guidance, parameter, and return format. No output schema exists, but the description sufficiently explains what to expect.

    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 100%. The description mentions the parameter in Args but adds minimal meaning beyond the schema's description. Baseline 3 is appropriate.

    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 uses specific verbs ('List') and resources ('mailbox folders'), and includes what it returns (message counts, unread counts). It clearly distinguishes from sibling tools which deal with emails, not folders.

    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?

    Explicitly states to use this tool to discover folders before listing/searching emails, providing clear context for usage. Does not explicitly mention when not to use it, but the sibling tools imply separation.

    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?

    Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds behavioral context: returns summaries, supports pagination (limit, offset), and default folder is INBOX. No contradictions.

    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?

    Well-structured with intro, parameter list, and examples. Slightly long due to full parameter listing, but clear and front-loaded with purpose.

    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 12 parameters and no output schema, description explains all inputs, return type (paginated summaries), and usage examples. Sufficient for an AI agent.

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

    Parameters4/5

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

    Schema coverage is 100%, so baseline is 3. The description adds value by explaining defaults, date format (ISO 8601), and response_format enum meanings, going beyond 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 it searches emails in a folder using IMAP search criteria, listing specific filters. It distinguishes itself from siblings like proton_get_email by noting that full content retrieval requires a separate tool.

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

    Usage Guidelines5/5

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

    Explicitly mentions when to use this tool (for searching/filtering) and when to use a sibling (proton_get_email for full content). Provides examples for common queries.

    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?

    While annotations already provide readOnlyHint and idempotentHint, the description adds that it returns full email content but NOT attachment binary data, which is valuable behavioral context beyond annotations.

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

    Conciseness5/5

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

    Description is well-structured with sections for purpose, prerequisites, args, returns, and examples. Every sentence adds value, no redundancy.

    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 no output schema, the description fully explains what the tool returns, what it does not (attachment binaries), and how to use it with examples. Completely addresses the tool's context.

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

    Parameters4/5

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

    Schema coverage is 100%, so baseline is 3. However, the description adds meaningful context with parameter descriptions and examples, clarifying that uid comes from search/list and showing usage patterns.

    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 single email's full content by its IMAP UID', specifying a specific verb and resource. It also distinguishes from sibling tools by naming them as prior steps.

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

    Usage Guidelines5/5

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

    Explicitly instructs to use proton_list_emails or proton_search_emails first to get the UID, providing clear context for when and how 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.

  • Behavior4/5

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

    Annotations already indicate readonly and idempotent. Description adds sorting order and pagination behavior, but does not elaborate further on rate limits or other constraints.

    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, front-loaded with purpose. Structured with Args/Returns/Examples. Every sentence is informative, no padding.

    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?

    Without output schema, the description explains return structure (email summaries, pagination metadata). Covers all aspects needed for a list tool with 4 optional parameters.

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

    Parameters4/5

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

    Schema coverage is 100%, baseline 3. Description adds context via examples and clarifies default values, but does not significantly extend parameter understanding beyond the schema.

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

    Purpose5/5

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

    The description clearly states the tool lists emails in a folder, sorted newest first with pagination support. It differentiates from siblings by noting that full bodies require proton_get_email.

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

    Usage Guidelines5/5

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

    Explicitly states when to use this tool (for summaries) and when to use a sibling (proton_get_email for full bodies). Provides examples to illustrate usage.

    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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