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norman2112

AgilePlace MCP Server

by norman2112

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v3.0.0

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of overlap or misselection. Its purpose is explicit: bulk-create cards on a board.

    Naming Consistency4/5

    The single tool uses a clear verb_noun camelCase name, createCards, but there is no broader naming pattern to evaluate consistency across a set.

    Tool Count2/5

    For an AgilePlace server, one tool covering only card creation is too narrow; agents will likely need other board, lane, and card lifecycle operations. The tool itself is substantial, but the server is under-scoped.

    Completeness2/5

    Only creation is exposed; there are no read, update, delete, search, or connect operations for cards or boards. Bulk creation is handled robustly, but most AgilePlace workflows hit dead ends.

  • Average 4.9/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 2 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
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  • 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

  • Behavior5/5

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

    Goes far beyond annotations. The description discloses partial success semantics (isError: false with failed[]), idempotency implications (created[] cards already exist), the tool's resolution of type labels, the cosmetic nature of header, and explicit prohibitions like 'Never invent board, type, or lane ids.' These behaviors are not inferable from annotations alone.

    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 long and dense, but each sentence provides essential operational detail for handling partial failures, rate limits, and parent-card connections. It is front-loaded with the core purpose and then systematically covers edge cases. Slightly more structured formatting (bullets) could improve readability, but nothing is wasteful.

    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 (bulk creation, partial success, retryAfter, parent linkage) and the presence of an output schema, the description is exceptionally complete. It explains return values, error handling, chunking, idempotency rules, and integration steps, leaving no significant ambiguity for an AI agent.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Although the schema already describes all parameters (100% coverage), the description enriches meaning by clarifying usage: 'type' is resolved to typeId, 'header' is cosmetic and not used for connections, 'clientKey' is echoed in responses, and 'boardId' accepts title or id. These are practical semantics that go beyond mere field descriptions.

    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 opens with 'Bulk-creates cards on one board,' a specific verb+resource statement that clearly identifies the tool's function. It also explicitly distinguishes from updates ('Use for new cards, not updates'), which helps differentiate even in the absence of sibling 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?

    Provides explicit guidance on when to use the tool ('Use for new cards, not updates') and how to handle failures ('resubmit only the failed[] cards'). It covers chunking (>40), partial success, retryAfter logic, and never resubmitting created[] clientKeys, giving clear directives for safe and correct usage.

    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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