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AKAddons

goalplanner-share-mcp

by AKAddons
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool serves a distinct purpose: craft builds import strings, decode reverses them, and list_supported_goals provides metadata. There is no functional overlap.

    Naming Consistency5/5

    All tool names follow a verb_noun snake_case pattern (craft_import_string, decode_import_string, list_supported_goals), consistent across the set.

    Tool Count5/5

    Three tools is an appropriate scope for a share utility: create, decode, and introspect. No unnecessary tools, and no obvious missing ones.

    Completeness5/5

    The tool surface covers the full workflow: constructing an import string, decoding an existing string, and checking supported goal types. No gaps for the stated purpose.

  • Average 4.5/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 25 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

  • This repository is licensed under BSD 2-Clause "Simplified" 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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  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

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

    Then . Browse examples.

  • Add related servers to improve discoverability.

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

  • Behavior5/5

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

    With no annotations, the description fully discloses behavior: default returns preview with rendering and warnings, confirm:true returns paste-ready code; explains GPSHARE1 vs GPSHARE2 wires, reuse-dedup for multi-section, and crafting conventions. This is comprehensive and compensates for lack of annotations.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is verbose with multiple paragraphs and detailed crafting conventions. While well-structured, it could be more concise. The essential information is front-loaded, but the crafting conventions might be better placed elsewhere.

    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 (two modes, nested parameters, two-phase workflow), the description covers all necessary context: preview vs confirm behavior, wire formats, dedup rules, and crafting guidelines. No output schema exists, but return values are clearly described.

    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 coverage is 100%, so baseline is 3. The description adds value by explaining the workflow implications (e.g., confirm flag behavior, dedup with targetDefault) and contextual details like wire formats, exceeding mere parameter explanations.

    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 purpose: 'Build a RuneLite Goal Planner import code from a structured goal spec'. It distinguishes two modes (single section vs multiple sections) and the confirm workflow, making it distinct from sibling tools (decode_import_string and list_supported_goals).

    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 provides explicit usage guidance: when to use single-section vs multi-section, the preview-then-confirm flow, and crafting conventions. It lacks explicit 'when not to use' statements but effectively differentiates from siblings through context.

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

  • Behavior4/5

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

    No annotations provided, so the description carries full burden. It discloses that the tool decodes codes, tolerates embedded text, and returns a breakdown of various elements. It does not mention side effects or error behavior, but for a read-only decoding tool this is sufficient.

    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 sentence followed by a short tip, both front-loaded and concise. Every part adds value without redundancy.

    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 no output schema, the description thoroughly explains the output format (readable breakdown with sections, goals, etc.) and input tolerance. It covers key behavioral aspects for a single-parameter decoding tool.

    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 100% and describes the 'code' parameter. The description adds practical context: 'Tolerates surrounding text — paste the whole message containing the code,' which aids agent understanding beyond the 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 'Decode' and the resource 'GPSHARE1:/GPSHARE2: code', and specifies the output as a readable breakdown with components like sections, goals, and dependencies. It implicitly distinguishes from siblings: craft_import_string (encoding) and list_supported_goals (listing goals).

    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 gives a specific usage tip: 'Tolerates surrounding text — paste the whole message containing the code.' It provides clear context but does not explicitly state when not to use or name alternatives, though sibling differentiation is implied.

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

  • Behavior4/5

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

    No annotations are provided, so the description carries full burden. It transparently discloses that the tool reports two types of goals and skill names, implying a read-only, informational operation with no side 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 two sentences, front-loaded with the purpose, and contains no unnecessary words. Every sentence adds value.

    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?

    Despite lacking an output schema, the description fully explains what the tool returns (goal types and skill names) and its decision-support purpose, making it complete for a simple listing tool.

    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?

    No parameters are defined in the input schema, so the description need not add parameter info. Baseline score of 4 applies as no param details are needed.

    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 'report' and the resource 'goal kinds' with specific details (auto-tracked vs. CUSTOM, plus skill names), and distinguishes from sibling tools like craft_import_string by its use case of deciding tracking.

    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 states when to use the tool ('Use this to decide whether a requested goal will track on the recipient's account'), providing clear context for usage without naming alternatives.

    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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