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cybrlou

Office 365 Email MCP

by cybrlou
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools serve entirely distinct purposes: one sends emails, the other verifies OAuth2 connectivity. There is no overlap or ambiguity.

    Naming Consistency5/5

    Both tools follow a consistent 'office365_verb_noun' pattern (send_email, test_connection), with no mixing of conventions.

    Tool Count4/5

    With only two tools, the server is extremely focused on sending emails and testing authentication. While this is appropriate for a minimal integration, it feels slightly under-scoped for a full Office 365 Email MCP.

    Completeness2/5

    The server only covers sending emails and connection testing, missing essential email operations like reading, searching, or managing folders. This is a significant gap for a service branded as 'Email MCP'.

  • Average 4.9/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 2 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 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.

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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 and destructiveHint, but description adds detail on checking environment variables and the return format (success/error strings), providing behavior 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 concise with two paragraphs, front-loaded with main purpose, and every sentence adds value without 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?

    For a zero-parameter diagnostic tool with annotations, the description fully explains behavior and return format, making it complete.

    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?

    Input schema has zero parameters, so schema_description_coverage is 100%. Baseline for 0 params is 4, and description adds no parameter info which is unnecessary.

    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?

    Description clearly states the tool verifies OAuth2 configuration by acquiring an app-only token. It specifies what it does not do (no email send, no data read), distinguishing it from sibling tool office365_send_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 advises 'Use this first when diagnosing setup problems,' and clarifies that it performs no email send and reads no data, guiding when to use versus alternatives.

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

  • Behavior5/5

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

    The description discloses behavioral traits beyond annotations: it confirms email sending (write operation), notes default save to sent items, attachment size limit (~3 MB), and error response format ('Error:'). Annotations show readOnlyHint=false, destructiveHint=false, idempotentHint=false, with 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.

    Conciseness5/5

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

    The description is well-structured with sections (summary, details, Args, Returns, Examples) and front-loads the core purpose. Every sentence is informative, concise, and earns its place.

    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 aspects: inputs, outputs, error handling, authentication, constraints, and examples. The sibling tool is minimal, so no additional differentiation needed.

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

    Parameters5/5

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

    Despite 0% schema description coverage, the description provides detailed parameter semantics in the Args section, explaining purpose, constraints, defaults, and examples for each parameter (to, subject, body, is_html, cc, bcc, sender, attachments, save_to_sent_items). This fully compensates for the lack of schema descriptions.

    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 'Send an email through Microsoft 365 via the Microsoft Graph API,' specifying the action, resource, and distinguishing from the sibling tool (office365_test_connection). It explicitly warns 'Don't use to read or search mail; this tool only sends.'

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

    Usage Guidelines5/5

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

    The description provides explicit when-to-use guidance (sending email) and when-not-to-use (reading or searching mail). It includes examples, constraints like attachment size limits, authentication details, and mentions the O365_SENDER env var. It also clarifies that it uses app-only OAuth2 flow.

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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