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elimyliu

timeverse-command-mcp

by elimyliu
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Python
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Server Quality Checklist

67%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    The three tools are clearly distinct: bash runs shell commands, node runs JavaScript, and python runs Python. No overlap in purpose.

    Naming Consistency5/5

    All tool names are single-word runtime names (bash, node, python), following a consistent and intuitive pattern.

    Tool Count5/5

    3 tools is a well-scoped set for a code execution server, covering the most common interpreters without being excessive.

    Completeness4/5

    The set covers bash, node, and Python, which are the most common execution environments. A minor gap is the lack of file-based script execution, but for inline code snippets it is largely complete.

  • Average 3.8/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 9 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • 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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Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

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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?

    No annotations provided; description only mentions streaming output. Does not disclose potential destructive actions (e.g., rm, dd) or other safety behaviors. Fails to provide adequate transparency for a command execution tool.

    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?

    Concise single sentence plus a list of common scenarios; no wasted words. Structured effectively for quick understanding.

    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?

    No output schema; description mentions streaming output but lacks details on exit codes, stderr handling, or security considerations. Minimum viable but could be more complete for a potentially dangerous tool.

    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 covers all 3 parameters with descriptions (100% coverage). Description adds no parameter-specific details beyond schema, so baseline score of 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?

    Description clearly states it executes commands in a local shell, supports streaming output, and lists common use cases (file operations, system info, CLI tools), distinguishing it from siblings node and python.

    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?

    Implicitly suggests use for shell commands via listed scenarios, but lacks explicit guidance on when to choose this tool over siblings (node, python). No alternatives or when-not-to-use mentioned.

    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?

    No annotations are provided, so the description carries full burden. It discloses that code executes locally via node -e, but does not mention potential side effects, security implications, sandboxing, or return value behavior. While not misleading, it lacks depth for a clear behavioral profile.

    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 a single concise sentence that front-loads the core action ('Execute single piece of code in local Node.js (node -e)') and adds relevant use cases. No redundant information.

    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 simplicity of the tool (2 parameters, no nested objects), the description covers purpose and typical use. However, it omits details about output format or error handling, which would be helpful since there is no output schema. Still, it is nearly complete for a straightforward execution tool.

    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 coverage is 100%, so the baseline is 3. The description does not add meaning beyond the schema: both 'code' and 'timeout' are already described adequately in the input schema properties. No additional parameter context is provided.

    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 executes a single piece of code in local Node.js (node -e) and specifies typical use cases like JavaScript calculations and JSON processing. It differentiates from siblings (bash, python) by focusing on JavaScript.

    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 mentions suitability for JavaScript calculations and JSON processing, implying when to use, but does not explicitly state when not to use or provide direct comparisons to sibling tools (bash, python). Guidance is implied rather than explicit.

    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 burden. It describes execution via 'python -c', implying a single code snippet in the local interpreter. It does not mention security or side effects but is clear about the execution mode.

    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 a single sentence, front-loaded with the action, and contains no unnecessary words. It is optimally concise.

    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 complexity (code execution) and lack of output schema, the description does not mention output format (stdout/stderr) or error handling. This leaves some gaps for the agent.

    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 coverage is 100%, so baseline is 3. The description adds context that code is passed as '-c' argument, but otherwise does not add significant meaning beyond the schema 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 clearly states the action ('execute a single piece of code'), the resource ('local Python 3 interpreter'), and the mode ('python -c'). It also lists specific use cases, distinguishing it from sibling tools like bash and node.

    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 indicates suitable use cases ('quick calculations, data processing, calling Python libraries'), which implies when to use this tool over alternatives. However, it does not explicitly state when not to use or mention exclusions.

    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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