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jonnybottles

M365 Roadmap MCP Server

by jonnybottles

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v0.4.0

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of confusing it with other tools. The tool's purpose is clear and unique, making disambiguation trivially perfect.

    Naming Consistency5/5

    The single tool name 'search_roadmap' follows a consistent verb_noun pattern, which is predictable and clear. With only one tool, the naming is internally consistent.

    Tool Count4/5

    While a single tool is at the low end of the range, it comprehensively covers the server's stated purpose of searching the M365 Roadmap. The tool is highly flexible with many optional filters, making the count appropriate for its narrow scope.

    Completeness5/5

    The search tool covers all expected operations for a roadmap lookup: browsing recent features, searching by keyword, filtering by product/status/cloud/date, and retrieving by specific ID. There are no obvious gaps for a read-only roadmap search service.

  • Average 4.8/5 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
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing
  • 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

  • Behavior5/5

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

    With no annotations provided, the description carries the full burden and does so excellently. It discloses key behaviors such as all filters being optional and combinable, feature_id being exclusive, partial matching for dates, and include_facets behavior with limit=0. This is substantial contextual detail.

    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 longer than average but well-organized: a summary paragraph followed by a categorized bullet list of use cases. Each line is purposeful and adds specific guidance, though it could be tightened slightly without losing meaning.

    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 (13 optional parameters, output schema, no annotations), the description covers all essential contexts: navigation, filtering, special cases, and combination examples. The presence of an output schema means return-value details are handled elsewhere, so nothing critical is missing.

    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 schema already provides 100% coverage with descriptions for every parameter, so baseline is 3. The description adds value by offering concrete usage examples (e.g., query='Copilot', product='Microsoft Teams') and clarifying combinability, which elevates it above the baseline.

    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 'Search the Microsoft 365 Roadmap for features matching keywords and filters' with a specific verb and resource. It also enumerates many distinct capabilities (keyword, product, status, cloud instance, feature_id, etc.) that differentiate it from generic search tools.

    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?

    It provides an explicit 'Use this tool to' list with concrete examples for each filter type, plus guidance on how filters combine and the default behavior when no filters are given. This gives the agent clear directives for when to invoke the tool.

    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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