Skip to main content
Glama
huzayfaSiddique

SandboxRunner

by huzayfaSiddique
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

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

  • Latest release: v0.1.1

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: run_code executes code snippets, list_supported_languages returns available runtimes, and get_execution_history provides a log of past runs. There is no functional overlap.

    Naming Consistency5/5

    All tool names follow the verb_noun pattern (run_code, list_supported_languages, get_execution_history) with consistent snake_case style.

    Tool Count5/5

    Three tools is a well-scoped set for a code sandbox server. It covers the essential operations: running code, querying supported languages, and reviewing execution history, without unnecessary extras.

    Completeness5/5

    The tool surface is complete for the stated purpose of running code in an isolated sandbox. It includes language discovery, code execution, and history auditing, with no obvious missing functionality.

  • 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
    • 9 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 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.

  • Add a glama.json file to provide metadata about your server.

  • 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.

  • Add related servers to improve discoverability.

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

  • Behavior4/5

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

    Discloses that only first 500 characters of code are stored, stdout/stderr recorded as byte sizes only, and order is most recent first. With no annotations, this provides useful behavioral context.

    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?

    Front-loaded with purpose and clear structure. The description is well-organized with paragraphs for use cases and return fields, though the return field list is somewhat redundant given the output schema.

    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 one parameter and existence of output schema, the description covers key aspects: what data is stored, what is omitted (full code, output content), and use cases. Minor omissions like pagination but overall 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%, so the schema already documents the limit parameter adequately. The description does not add extra semantics beyond what the schema provides.

    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 retrieves a log of recent code executions by run_code, most recent first. It distinguishes itself from siblings run_code (which executes code) and list_supported_languages.

    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 concrete use cases: reviewing past code runs, checking success, auditing. No explicit when-not-to-use, but context makes it clear this is for history retrieval.

    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?

    No annotations are provided, so the description carries full burden. It explains the return format (list of dicts with three fields) and that it has no side effects. It doesn't mention auth or rate limits, but for a read-only listing tool that is acceptable.

    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?

    Extremely concise: three short paragraphs. Front-loads the action and result, then usage advice, then return schema. Every sentence earns its place with no redundancy.

    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 zero parameters and the availability of an output schema (presumably documenting the return fields), the description is complete. It describes the output structure clearly, compensating for any potential gaps in the output schema.

    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?

    There are zero parameters. The description adds no parameter info, but the baseline for 0 params is 4. No need for further detail.

    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 every programming language and Docker image for the sandbox. It distinguishes itself from sibling tools (run_code, get_execution_history) by focusing on discovery of supported languages.

    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 'Call this first if you're unsure what values are valid for the `language` parameter of `run_code`.' This provides clear context for when to use it. It also notes 'Takes no arguments,' which is helpful. Lacks explicit when-not-to-use or alternative suggestions, but sufficient.

    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?

    With no annotations, the description fully discloses isolation level, resource caps (256 MB, 0.5 CPU), ephemeral nature, return structure, and limitations. No contradictions.

    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?

    Description is thorough but well-organized; each sentence adds value. Could be slightly shorter, but effective.

    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?

    Covers purpose, behavior, parameters, return values, and limitations comprehensively. Output schema is described, so no 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?

    Schema descriptions are already detailed, but the description adds context like max code size 50KB, case-insensitive language, and timeout ceiling 30s, which are not in 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 executes Python or C++ code in an isolated container and returns output. It distinguishes itself from siblings like list_supported_languages and get_execution_history by focusing on 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 explicit use cases (exploratory code, verification) and a warning about not being a hardened sandbox. Does not explicitly compare to siblings, but context makes it clear.

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

GitHub Badge

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.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

sandbox_runner MCP server

Copy to your README.md:

Score Badge

sandbox_runner MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/huzayfaSiddique/sandbox_runner'

If you have feedback or need assistance with the MCP directory API, please join our Discord server