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JackInSightsV2

Microsoft 365 & Azure MCP Server

by JackInSightsV2
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.1.0

  • Disambiguation5/5

    The two tools target completely different APIs: Azure CLI vs Microsoft Graph, with no overlap in functionality.

    Naming Consistency3/5

    One uses a verb_noun pattern ('execute_azure_cli_command') while the other is just noun ('graph_command'), lacking a consistent action prefix.

    Tool Count2/5

    Only 2 tools for a broad domain like Microsoft 365 & Azure is insufficient; the server feels skeletal and under-scoped.

    Completeness1/5

    The server lacks any domain-specific operations; generic command execution leaves massive gaps for any practical use.

  • Average 3.4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 18 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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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

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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.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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?

    With no annotations, the description carries full burden. It lacks disclosure of side effects, permissions, error handling, return format, or synchronization behavior. Only mentions command prefix and policy, which is minimal.

    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?

    Two sentences, front-loaded, no fluff. However, conciseness sacrifices completeness for a tool that executes arbitrary commands.

    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?

    For a tool that executes arbitrary CLI commands with no output schema, the description is notably incomplete. It omits details on success/error responses, timeouts, how the execution policy works, and comparisons with sibling tool graph_command.

    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 description coverage is 100%, so the schema already documents the parameter. The description adds the constraint that commands must begin with 'az', but this is partially redundant since the schema implies CLI commands. No significant additional semantics 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 the verb 'execute' and the resource 'Azure CLI command'. It distinguishes from the sibling tool 'graph_command' by specifying the command prefix 'az', indicating Azure CLI focus.

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

    Usage Guidelines3/5

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

    Provides some guidance ('commands must begin with az', 'subject to execution policy') but does not explicitly specify when to use this tool versus alternatives like graph_command, nor does it provide exclusion criteria.

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

  • Behavior2/5

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

    With no annotations provided, the description carries full responsibility for disclosing behavior, but it only states the action and methods. It fails to mention key aspects such as authentication requirements, rate limits, response format (likely raw JSON), idempotency of GET vs write methods, or error handling. This lack of behavioral context limits the agent's ability to anticipate tool effects.

    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 a single, 14-word sentence that conveys the essential action without redundancy. Every word contributes meaning, and no extraneous information is present. It is well front-loaded with the core functionality.

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

    Completeness3/5

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

    Given the tool's generic nature (calling arbitrary Graph endpoints with 3 parameters and no output schema), the description is adequate but incomplete. It does not explain what the response contains (e.g., JSON payload, status codes), how pagination works, or any constraints. However, for a straightforward interface, an agent familiar with Microsoft Graph might infer these details.

    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 description coverage is 67% (command and data have descriptions; method lacks one). The description adds value by specifying the Graph API version ('v1.0'), which is not in the schema. The schema's description of 'command' as 'Graph path such as 'me', 'users', or 'groups'' is clear and helpful. Together, they provide solid parameter context.

    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 'Call a Microsoft Graph v1.0 endpoint with GET, POST, PUT, PATCH, or DELETE' clearly states the specific verb ('call'), resource ('Microsoft Graph v1.0 endpoint'), and supported HTTP methods. This distinguishes it from the sibling tool 'execute_azure_cli_command', which targets Azure CLI commands rather than Graph API calls.

    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. It does not mention prerequisites, context, or when not to use it (e.g., for non-Graph calls). The sibling tool 'execute_azure_cli_command' is not referenced, leaving the agent without clear decision logic.

    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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