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therealjohn

Microsoft Teams MCP Server

by therealjohn
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Python
Remote

Server Quality Checklist

50%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool 'send-notification' has a clear and singular purpose, making it impossible for an agent to misselect between tools.

    Naming Consistency5/5

    The single tool name 'send-notification' follows a consistent verb-noun pattern. Since there is only one tool, there is no inconsistency to evaluate, and the naming is straightforward and predictable.

    Tool Count2/5

    A single tool is too few for a server named 'Microsoft Teams MCP Server', which suggests a broader domain like messaging, collaboration, or team management. This minimal set likely leaves significant gaps in functionality, making it inappropriate for the implied scope.

    Completeness1/5

    The tool set is severely incomplete for a Microsoft Teams server. It only covers sending notifications, missing essential operations such as reading messages, managing channels, handling meetings, or interacting with users, which are core to the Teams domain and will cause agent failures.

  • Average 2.7/5 across 1 of 1 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

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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 markdown formatting support, which adds some behavioral context about message handling. However, it lacks details on delivery mechanisms, user targeting, error handling, or response format, leaving significant gaps for a notification-sending tool.

    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 front-loaded with the core purpose. The formatting details are relevant but could be more efficiently integrated. No wasted sentences, though it could be slightly more structured for clarity.

    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, 0% schema coverage, and no output schema, the description is incomplete. It covers message formatting but misses parameter explanations, behavioral traits like delivery guarantees, and expected outcomes. For a tool with two required parameters, this is inadequate.

    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 does not explain the 'message' or 'project' parameters at all—no semantics, examples, or constraints. The formatting advice applies generally to messages but doesn't clarify parameter roles or usage.

    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: 'Send a notification message to the user.' It specifies the action (send) and resource (notification message), though it doesn't distinguish from siblings since none exist. The mention of markdown formatting adds specificity about message capabilities.

    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 exclusions. It only describes formatting features (markdown, backticks, square brackets) without contextual usage advice. Since there are no sibling tools, this is less critical but still a gap.

    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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  • Evaluate tool definition quality.

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