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chrassy

klanex-mcp

by chrassy
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JavaScript
Remote

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: get_execution fetches execution state, get_usage reports account usage, and replay_execution re-runs a terminal execution. No ambiguity.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern in snake_case: get_execution, get_usage, replay_execution. No deviations.

    Tool Count3/5

    Only 3 tools for an execution management platform is on the low end. While focused, typical CRUD operations would benefit from more tools (e.g., create, list, cancel).

    Completeness2/5

    Obvious gaps exist: no tool to create a new execution (only replay), no list/cancel functionalities. The workflow is incomplete for full lifecycle management.

  • Average 4.3/5 across 3 of 3 tools scored.

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

    • 0 of 1 community issues answered or closed in the last 6 months
    • 5 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.

  • This repository includes a glama.json configuration file.

  • 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.

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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

  • Behavior4/5

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

    Discloses header redaction and retry handling behavior, which are critical for agent decision-making. No annotations exist, so description carries full burden and does so well.

    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?

    Three concise sentences, each providing distinct value: purpose/outputs, behavioral guidance, and constraint. No fluff, well front-loaded.

    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?

    Without an output schema, the description adequately explains return fields and key behaviors (redaction, retry). Adequate for a simple fetch tool with one parameter.

    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 already fully documents the single parameter with description 'the execution ID (exe_...)'. Description adds no additional semantic detail 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?

    Clearly states the verb 'Fetch' and resource 'execution by ID', enumerates specific output fields (status, attempts, response/error), and implicitly distinguishes from siblings get_usage and replay_execution.

    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?

    Provides clear context for fetching execution state and includes an important 'when not to act' hint for retryable failures, but does not explicitly contrast with sibling tools.

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

  • Behavior3/5

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

    Discloses that it returns usage data for the current month and includes fields like overage info. No annotations exist, so description carries full behavioral burden. Lacks details on authentication, rate limits, or side effects, but as a read-only query with no parameters, it 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?

    Two efficient sentences: first defines output, second provides usage advice. 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?

    Describes output fields in plain language despite no output schema. Could be more precise about data format, but sufficient for agent to understand return value.

    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 exist (0 params, 100% schema coverage). Baseline of 4 applies as the description need not compensate for parameter documentation.

    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?

    Clearly states it reports account plan and execution usage for the current calendar month, listing specific metrics (used, included_executions, remaining, overage allowed). Distinct from sibling tools like get_execution which likely handle individual executions.

    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 advises checking before large batches to avoid hitting a hard quota, providing a clear use case. Does not specify when not to use, but context is sufficient.

    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?

    The description discloses key behavioral traits: it re-runs with same target, byte-exact original payload, same credentials, fresh attempt counter. It states it is deliberately not idempotent and only works for terminal (SUCCEEDED or FAILED) executions. Since no annotations are provided, these details are critical and well-provided.

    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 that efficiently convey everything needed. No wasted words; front-loaded with core action.

    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 simplicity (single parameter, no output schema, no nested objects), the description covers purpose, constraints on input, behavioral implications, and even distinguishes from siblings implicitly via context. No critical gaps.

    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 schema covers the parameter with minimal description. The tool description adds the requirement that the execution must be terminal (SUCCEEDED or FAILED), which is beyond the schema and adds semantic value. With 100% coverage, description provides meaningful additional context.

    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 the specific verb 're-run' and identifies the resource as 'terminal execution'. It clearly states the tool creates a brand-new execution with the same target and payload, which distinguishes it from the sibling get_execution (read) and get_usage (read).

    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 this is the 'outage-recovery path' and that failed calls can be re-executed without regenerating payload. It also warns that each call creates a new execution and is not idempotent, guiding the user on when to use and the side effect. However, it does not explicitly exclude use cases like modifying the payload.

    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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