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henrikogaard

Infomaniak kSuite MCP Server

by henrikogaard
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Server Quality Checklist

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

  • Latest release: v1.1.0

  • Disambiguation5/5

    Each tool serves a distinct purpose: creating a paste, reading a paste, and listing available tools. There is no overlap or ambiguity between them.

    Naming Consistency4/5

    Two tools follow the pattern 'kpaste_<action>', while the help tool uses 'infomaniak_help', a slightly different prefix. The pattern is mostly consistent but has a minor deviation.

    Tool Count3/5

    Three tools is on the lower end but acceptable for a focused service like kPaste. However, the server name implies a broader kSuite offering, making the count feel slightly insufficient.

    Completeness2/5

    The server only covers kPaste (create/read) and a help tool, missing other kSuite services. For kPaste itself, there is no list or delete capability, leaving gaps in lifecycle management.

  • Average 4.4/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 5 commits in the last 12 weeks
    • Last stable release on
    • 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",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

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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?

    Annotations indicate a write operation (readOnlyHint=false) with side effects (openWorldHint=true). The description adds significant behavioral context: encryption, ephemerality, and the decryption key being URL fragment (client-side). No contradiction with annotations.

    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?

    Two sentences, both front-loaded with core purpose: creation, encryption, and return format. 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.

    Completeness4/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    The description covers the tool's purpose, security model, and usage constraint. While it doesn't detail each parameter or the output schema, the schema already covers those. Missing explicit mention of burn_after_reading behavior, but 'ephemeral' implies it. Adequately complete for a tool with rich schema and annotations.

    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?

    Input schema has 100% coverage with descriptions for all 4 parameters (content, password, expiration, burn_after_reading). The description adds no parameter-specific details beyond schema, so baseline 3 is appropriate.

    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 creates an encrypted, ephemeral paste on kPaste, specifying security details (zero-knowledge, AES-256-GCM) and the return of a URL with decryption key. This distinguishes it from kpaste_read which would read pastes.

    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 includes a usage constraint ('only use with a trusted transcript and MCP client') and implies sharing secrets. However, it does not explicitly compare to siblings like kpaste_read or state when not to use this tool.

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

  • Behavior5/5

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

    Adds context beyond annotations by explaining why it is destructive (burn-after-reading consumption) and requiring confirmation, fully addressing the destructiveHint and readOnlyHint.

    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?

    Two concise sentences that front-load the purpose and immediately follow with critical behavioral and usage details, no wasted words.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness4/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the presence of an output schema (likely explaining return values) and the tool's straightforward read operation, the description is sufficiently complete; minor gap could be explicit mention of decryption process.

    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?

    Schema has 100% coverage with descriptions for all three parameters; the description adds little beyond reiterating the confirmation phrase, so value is marginal.

    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 reads and decrypts a kPaste URL using the fragment key, distinguishing it from sibling tools like kpaste_create which creates pastes.

    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?

    Specifies the need for an exact confirmation phrase and notes the destructive behavior for burn-after-reading pastes, providing clear usage context; does not explicitly state when not to use.

    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?

    Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds useful details about the output content (safety labels, workflow suggestions) and confirms it's a listing, not a mutation.

    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?

    Two sentences, no wasted words. First sentence describes output, second gives usage guidance. Perfectly front-loaded.

    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 fully covers what the tool does given its purpose as a help tool. Output schema exists but description still enumerates expected content. It is complete for an overview tool with two optional params.

    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% with descriptions for both parameters (service enum, include_tools boolean). The description adds that it includes usage hints and workflow suggestions, enhancing meaning beyond 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 it lists available Infomaniak MCP tools with details like service groups, argument names, safety labels. It distinguishes from siblings (kpaste_create, kpaste_read) which are specific operational tools.

    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?

    Explicitly says to use this first when the user asks what the MCP can do or which tools exist. Provides clear context but does not explicitly mention when not to use, though siblings are obvious 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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