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capsulerun

Capsule server

by capsulerun
OverviewSchemaRelated ServersScoreDiscussions
Rust
Remote

Server Quality Checklist

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

  • Latest release: v0.1.2

  • Disambiguation5/5

    The two tools have clearly distinct purposes: execute_javascript for JavaScript code execution and execute_python for Python code execution. There is no overlap or ambiguity between them, as each targets a different programming language in a similar sandbox environment.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun naming pattern (execute_javascript and execute_python), using the same verb 'execute' followed by the language name. This makes the naming predictable and easy to understand across the tool set.

    Tool Count2/5

    With only 2 tools, the server feels thin for a general-purpose code execution server. While it covers two popular languages, the scope suggests potential for more languages (e.g., Ruby, Go) or related operations (e.g., list_sandboxes, kill_execution), making the current count insufficient for broad utility.

    Completeness3/5

    The server provides execution capabilities for JavaScript and Python, which are core to its domain of code execution. However, there are notable gaps: no tools for managing sandboxes (e.g., creating, listing, or terminating), handling dependencies, or supporting other common languages, limiting agent workflows to basic execution without lifecycle control.

  • Average 4.1/5 across 2 of 2 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
    • 3 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under Apache 2.0.

  • 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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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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: the code runs in a 'secure isolated WebAssembly sandbox' (implying safety and isolation), and it returns both 'standard output (console logs) and the last evaluated expression.' This covers execution environment and output behavior well, though it lacks details on timeouts, memory limits, or error handling.

    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 front-loaded with the core purpose and efficiently uses two sentences to cover execution context and return values. Every sentence earns its place with no wasted words, making it highly concise and well-structured.

    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 tool's moderate complexity (executing code in a sandbox), no annotations, and no output schema, the description does a good job of covering the essential context: what it does, the environment, and what it returns. However, it could be more complete by addressing potential limitations like execution time or security constraints.

    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 description coverage is 100%, so the schema already documents the single 'code' parameter. The description adds minimal value by reiterating that 'Standard output and the final expression are returned,' but doesn't provide additional syntax, format, or constraints beyond what the schema states. This meets the baseline for high schema coverage.

    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 specific action ('Execute JavaScript code') and resource ('in a secure isolated WebAssembly sandbox'), distinguishing it from the sibling tool execute_python by specifying the programming language. It provides a complete picture of what the tool does.

    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 context by mentioning the sandbox environment, but it doesn't explicitly state when to use this tool versus execute_python or other alternatives. No guidance on prerequisites or exclusions is provided, leaving usage decisions to inference.

    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 provided, the description carries full burden and does well by disclosing key behavioral traits: secure isolated sandbox environment, return of both stdout and last expression, and language limitations (no C extensions). However, it doesn't mention potential execution time limits, memory constraints, or error handling behavior.

    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 appropriately sized with three sentences that are front-loaded and efficient. Each sentence adds essential information about execution environment, return values, and limitations without any wasted words or redundancy.

    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 tool's complexity (code execution in sandbox) with no annotations and no output schema, the description does well by covering execution environment, return values, and language constraints. However, it lacks details about error responses, execution limits, or security implications that would make it fully complete.

    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 description coverage is 100% with one parameter clearly documented, so the baseline is 3. The description adds minimal value beyond the schema by mentioning that 'Standard output and the final expression are returned,' which slightly elaborates on the output behavior but doesn't provide additional parameter-specific details.

    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 specific action ('Execute Python code') and resource ('in a secure isolated WebAssembly sandbox'), distinguishing it from the sibling execute_javascript tool by specifying Python execution. It provides precise details about the execution environment and language constraints.

    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 implicitly suggests usage for Python code execution with constraints ('pure Python only, no C extensions'), but does not explicitly state when to use this tool versus execute_javascript or other alternatives. It provides clear context about limitations but lacks explicit comparative guidance.

    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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