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co-native-ab

graphdo-ts

Official
by co-native-ab

Server Quality Checklist

67%
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  • Latest release: v0.0.0

  • Disambiguation5/5

    Each tool addresses a distinct aspect of the authentication lifecycle: login for signing in, logout for signing out, and auth_status for checking the current state. There is no overlap or ambiguity between them.

    Naming Consistency4/5

    Tool names are short and predictable, but 'auth_status' is a noun phrase while 'login' and 'logout' are verbs. The pattern is mostly consistent, though 'get_auth_status' would improve verb-noun consistency.

    Tool Count5/5

    Three tools is the minimum needed to cover the authentication lifecycle, and each tool serves an essential role. The scope is narrow but well-filled, with no unnecessary additions.

    Completeness5/5

    The set covers the full authentication domain: login, logout, and status checking. There are no missing operations for the stated purpose, making the surface complete for authentication management.

  • Average 4.7/5 across 3 of 3 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 failing
  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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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, and the description reinforces this with 'Check'. It adds meaningful context by enumerating the specific items returned (logged-in user, scopes, server version), which goes beyond the annotation. No contradiction with 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?

    The description is neatly packed into two sentences: the first lists the checked items, the second gives a usage recommendation. No filler or redundancy.

    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?

    The tool is conceptually simple (no parameters, read-only) and the description lists the key return fields, which is adequate for an agent to decide whether to call it. It does not describe output format or error behavior, but given its simplicity and the fact that annotations cover safety, it is sufficiently 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?

    The input schema has zero parameters, so the baseline of 4 is appropriate. The description does not need to explain parameter semantics because there are none to document.

    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 states a specific verb 'Check' and lists the exact resources (authentication status, logged-in user, configured todo list, granted scopes, server version), making the tool's purpose unmistakable and clearly distinguishing it from sibling login/logout tools.

    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?

    The description explicitly recommends this as a good first tool when diagnosing issues or understanding the setup, giving clear when-to-use context. It does not explicitly mention alternatives or when not to use it, but the sibling context (login/logout) implies this is the pre-check before actions.

    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 goes beyond annotations by disclosing significant behavioral traits: it opens a browser for interactive sign-in, instructs the agent not to ask for permission first, and explains that a successful login enables all other tools. This adds valuable context beyond the structured 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?

    The description is three sentences long, front-loaded with the core purpose, and every sentence adds value: purpose, usage directive, and behavioral consequence. There is no redundant or filler content.

    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 an authentication tool with no parameters and no output schema, the description covers all essential aspects: what it does, when to call it, what happens during execution, and the resulting effect on other tools. It is complete and appropriately scoped.

    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 tool has zero parameters, so the schema trivially covers 100%. Per rubric, a baseline of 4 is appropriate, and the description does not need to add parameter semantics.

    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's function with a specific verb and resource: 'Sign in to Microsoft Graph.' It also specifies the mechanism (opens a browser for interactive sign-in) and distinguishes itself from sibling tools like logout and auth_status.

    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?

    It provides explicit when-to-use guidance: 'Call this tool directly whenever authentication is needed - do not ask the user for permission first.' It also implies that after login other tools work automatically. However, it does not explicitly name alternatives or state when not to use it, falling short of a 5.

    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 that cached tokens are cleared, adding behavioral detail beyond the annotations' destructive and idempotent hints. It aligns with annotations and explains the consequence of logout.

    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?

    Two sentences, front-loaded with the verb and action, with no filler. Every sentence earns its place, providing the action, the effect, and the next step efficiently.

    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, no-output-schema tool, the description fully covers what the tool does, the effect on cached tokens, and the required next step. It is complete and self-contained.

    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?

    With zero parameters, the description appropriately focuses on behavior rather than parameter syntax. The schema already covers all (non-existent) parameters, so the baseline of 4 applies.

    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 action ('Sign out of Microsoft Graph') and the resource affected, distinguishing it from sibling tools like login and auth_status. It is a specific verb+resource statement with no ambiguity.

    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 explicitly specifies that after logging out, the login tool must be used to re-authenticate, providing a direct alternative and next-step guidance. This frames the tool's usage context clearly.

    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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Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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