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getArbor-dev

arbor

by getArbor-dev
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Server Quality Checklist

75%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: analyze_impact focuses on assessing the blast radius of a node change, while get_logic_path traces dependencies and usage in a call graph. There is no overlap or ambiguity between these functions.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (analyze_impact, get_logic_path) with clear, descriptive names. The naming style is uniform and predictable across the set.

    Tool Count2/5

    With only two tools, the server feels thin for a domain like code or system analysis, where more operations (e.g., for managing nodes, viewing graphs, or updating logic) might be expected. This limited set could hinder agent workflows.

    Completeness2/5

    Inferring a domain of code or system dependency analysis, the toolset is severely incomplete. It lacks basic CRUD operations (e.g., create, update, delete nodes) and essential functions like listing or searching dependencies, leaving significant gaps for agent tasks.

  • Average 2.9/5 across 2 of 2 tools scored.

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

    • 2 of 3 community issues answered or closed in the last 6 months
    • 96 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.

  • 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.

  • This repository includes a glama.json configuration file.

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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

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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 the full burden of behavioral disclosure. It mentions analyzing impact but doesn't specify what the analysis entails (e.g., computational cost, side effects, permissions required, or output format). This leaves significant gaps in understanding the tool's behavior.

    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, efficient sentence that directly states the tool's purpose without any fluff or redundancy. It is appropriately sized and front-loaded, making it easy to parse quickly.

    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 the complexity of impact analysis, no annotations, and no output schema, the description is incomplete. It doesn't explain what 'blast radius' entails, the nature of the analysis, or what results to expect, leaving the agent with insufficient context for effective use.

    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?

    The input schema has 100% description coverage, with 'node_id' documented as 'ID or name of the node to analyze'. The description adds no additional parameter semantics beyond this, so it meets the baseline of 3 for high schema coverage without extra value.

    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: 'Analyzes the impact (blast radius) of changing a specific node.' It specifies the verb ('analyzes') and resource ('impact of changing a specific node'), though it doesn't explicitly differentiate from the sibling tool 'get_logic_path' (which might retrieve paths rather than analyze impact).

    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, such as the sibling tool 'get_logic_path'. It lacks context on prerequisites, scenarios where this analysis is needed, or any exclusions, leaving the agent without usage direction.

    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 the full burden of behavioral disclosure. It mentions tracing and finding dependencies/usage, which suggests a read-only analysis operation, but doesn't clarify if it's safe, has side effects, requires permissions, or details output format (e.g., graph structure, depth limits). This leaves significant gaps for a tool with potential complexity.

    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, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy to parse quickly.

    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 and no output schema, the description is incomplete for a tool that traces call graphs. It lacks details on behavioral traits (e.g., safety, performance), output format, or how it differs from siblings, making it inadequate for an agent to fully understand usage without additional context.

    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?

    The input schema has 100% description coverage, clearly documenting the single parameter 'start_node'. The description adds context by specifying it traces 'dependencies and usage of a function or class', which aligns with the schema but doesn't provide additional syntax or format details beyond what's already covered.

    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 with specific verbs ('traces', 'find') and resources ('call graph', 'dependencies and usage', 'function or class'), making it easy to understand what it does. However, it doesn't explicitly differentiate from its sibling tool 'analyze_impact', which might have overlapping or related functionality.

    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 its sibling 'analyze_impact' or any alternatives. It implies usage for tracing dependencies and usage, but lacks explicit context, prerequisites, or exclusions, leaving the agent to infer appropriate scenarios.

    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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