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teamsparta-biz

padlet-mcp

by teamsparta-biz
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: list_boards returns a collection of boards, while get_board returns the full contents of a single board. There is no overlap in their functionality.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern: list_boards and get_board. The naming style is uniform and predictable.

    Tool Count3/5

    With only two tools, the server feels thin, but it may be intentionally scoped for read-only board access. The count is borderline, not clearly excessive or insufficient.

    Completeness3/5

    The tool surface covers listing boards and fetching full board contents, but lacks any create, update, or delete operations. This is a notable gap for a Padlet integration, though it may work for read-only use cases.

  • Average 4/5 across 2 of 2 tools scored. Lowest: 3.4/5.

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

    • No community issues in the last 6 months
    • 11 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.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

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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?

    With no annotations provided, the description carries the full burden. It states the tool returns all sections, posts, and comments, but provides no details on side effects, return structure, permissions, pagination, or error behavior. This is a significant gap for a read tool with complex output.

    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 concise sentences, front-loaded with the primary purpose and followed by a key parameter clarification. Every sentence earns its place, with no redundant information.

    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 has one parameter, no output schema, and no annotations, the description covers the essential purpose and parameter origin. However, it omits details on the return format (beyond 'organized form'), potential large result sets, or error cases, leaving gaps for an agent to fully understand invoking this 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?

    The parameter board_id has zero schema description coverage, but the tool description compensates by explaining it is a 16-digit ID from list_boards, not the URL slug. This adds critical semantic meaning beyond the schema's minimal type/required fields.

    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 fetches all sections, posts, and comments of a single board and returns them in an organized form. This is a specific verb+resource that distinguishes it from the sibling list_boards, though it does not explicitly contrast the two.

    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?

    The description implies usage by explaining that board_id is obtained from list_boards, suggesting a sequential workflow. However, it does not explicitly state when to choose this tool over alternatives or provide exclusions, so guidance is only 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?

    With no annotations, the description carries the transparency burden. It discloses that the tool lists boards accessible with the current API key, returns id, title, and public URL, and explains the URL-to-ID mapping utility. It does not explicitly state 'read-only' but the list semantics imply no side effects. Some detail like pagination or error cases is missing, but for a simple read operation this is adequate.

    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 with no fluff. The first sentence states the core purpose, the second sentence explains the return fields and a practical use case. Every sentence earns its place and the structure is clear.

    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 simple list tool with zero parameters and an existing output schema, the description covers the essential behavior (list boards, fields returned, and the URL-to-ID lookup scenario). It is complete for its complexity, though it omits edge cases like pagination, which are not critical for this 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?

    The tool has zero parameters, and the schema is empty. The description correctly avoids discussing parameters. The baseline for 0 params is 4, and no additional semantic value is needed since there are no inputs to explain.

    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 uses a specific verb ('반환한다' / returns) and resource ('보드(Padlet) 목록' / board list), and clearly states the scope ('현재 API 키로 접근 가능한' / accessible with current API key). It also distinguishes from the sibling tool by mentioning the 16-digit board ID used in get_board, showing this tool provides the mapping between URL and ID.

    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 a clear use case: when you only know a padlet.com URL, use this list to compare web_url and find board_id. This implicitly tells the user when to use list_boards, though it does not explicitly say 'use get_board when you already have the ID'.

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

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