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ronaldoribeirosm

cloudbeds-mcp-server

by ronaldoribeirosm
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of ambiguity between tools. The tool's purpose is clearly distinct from any potential future tools.

    Naming Consistency5/5

    The single tool name follows a clear verb_noun pattern (configure_cloudbeds_api_key) and is descriptive. With only one tool, consistency is trivially maintained.

    Tool Count2/5

    The server has 1 tool, which is too few for its apparent scope of managing Cloudbeds data. The description explicitly mentions future tools for occupancy, guests, and reservations, but those are not present.

    Completeness1/5

    The tool surface is severely incomplete. Only API key configuration is provided, with no operational tools for reservations, guests, or occupancy, making the server non-functional for its intended domain.

  • Average 4.7/5 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

  • 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.

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  "$schema": "https://glama.ai/mcp/schemas/server.json",
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  ]
}

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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 carries the transparency burden. It discloses that a real API call is made for validation, credentials are saved encrypted to a specific file path, the API key is never returned in this or future responses, and a restart is required. This is exemplary disclosure of side effects and security behavior.

    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 well-structured in two short paragraphs, and every sentence adds value: purpose, input location, validation process, security guarantee, and next steps. It is slightly longer than strictly needed but remains efficient and readable.

    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?

    The description covers the full context: when to use, what inputs are needed and where to find them, what happens during validation, where credentials are stored, the security guarantee about the key, and the necessary post-step (restart). The output schema exists and the description mentions the confirmation response, so nothing is missing.

    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?

    The input schema has zero descriptions for its two parameters, but the description compensates completely by explaining both: the API key and Property ID, including where to find the Property ID in the Cloudbeds panel. It also implies both are required and that they are validated together.

    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 configures the Cloudbeds connection on first run, with a specific verb and resource. It distinguishes this from a generic API call by detailing the validation, encryption, and restart steps, making the purpose unmistakable.

    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 says 'na primeira execução' (on first run) and instructs to restart the MCP server afterward to activate dependent tools. This provides clear when-to-use context, though it doesn't mention explicit alternatives because no siblings exist.

    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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