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realugbun

AgentExecMPC

by realugbun
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool targets a distinct function: executing code, installing packages, and running shell commands. There is no overlap in their purposes.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun snake_case pattern (execute_code, install_package, shell). 'Shell' is a noun but functions as a clear command name.

    Tool Count5/5

    With only 3 tools, the server is tightly scoped for code execution and environment management. Each tool serves a necessary role without clutter.

    Completeness4/5

    The set covers core operations: running code, installing packages, and shell access. A minor gap is the lack of package uninstallation or listing, but the essential workflows are present.

  • 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
    • 0 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

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

    MCP servers without a LICENSE cannot be installed.

  • 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?

    With no annotations, the description carries full burden. It discloses key traits: sandboxed execution, configurable timeout (60s default, 300s max), working directory support, automatic cleanup, and error handling. However, it omits details like network access restrictions or persistency of file changes.

    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?

    The description is well-structured with sections (intro, features, args, returns, examples) and uses bullet points. It front-loads the purpose. However, it is somewhat verbose; e.g., 'sandboxed execution' appears twice and some feature points could be merged.

    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 no output schema, the description explains returns (stdout, stderr, exit code, duration, success status). It covers timeout, working directory, error handling, and command constraints (64KB limit from schema, but not in description). It lacks mention of input size limit and does not detail sandbox restrictions, but overall is fairly 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 0% for the top-level parameter 'request', so the description must compensate. It lists 'command, timeout, and working directory' in Args and provides examples, but does not explain each parameter's semantics beyond what the schema already provides. The examples add value, but not enough to fully compensate.

    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 'Execute shell commands in a secure sandboxed environment' with specific verb-resource. It distinguishes from sibling tools 'execute_code' and 'install_package' by focusing on arbitrary shell commands for file operations, system commands, and scripting.

    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 provides clear context: 'Perfect for file operations, system commands, and shell scripting tasks.' It implicitly differentiates from siblings but does not explicitly state when not to use or provide alternative suggestions.

    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 were provided, so the description must disclose behavioral traits. It mentions version pinning, progress tracking, dependency resolution, extended timeout, and error reporting. This provides good insight, though it does not explicitly state if the operation is safe or destructive.

    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?

    The description is well-structured with sections and examples, but it is somewhat verbose. The key information is front-loaded, though some repetition could be trimmed.

    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 and good schema descriptions, the description is mostly complete. It mentions the return type ExecutionResponse, which compensates for the lack of an output schema. However, it could include more detail on error handling or preconditions.

    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?

    Parameter descriptions in the schema are detailed, so schema coverage is high (100%). The tool description adds value by summarizing version pinning and providing examples beyond the schema, earning a 4.

    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 installs packages using pip, npm, or Go modules with version control. The verb 'install' and resource 'packages' are specific and distinguish it from siblings execute_code and shell.

    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 provides clear context for when to use the tool (package installation across environments) but lacks explicit exclusions or alternatives. Knowledge of siblings suggests alternative tools for code execution or shell commands.

    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 behavioral traits: temporary files, isolated environments, proper cleanup, timeout handling, non-persistent code, and secure isolation. 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.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is well-structured with sections, bullet points, and examples. Every sentence adds value, and the main action is front-loaded. No redundant text.

    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's complexity (multiple languages, timeout, isolation), the description covers all key aspects: supported languages, features, execution details, and return type. No output schema, but the description explains the response structure.

    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 single parameter 'request' is a nested object with its properties described in the schema. The description adds value by listing the included parameters (code, language, timeout), providing examples, and explaining features, but does not add significant meaning beyond the schema's subfield 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 tool executes code snippets in Python, Node.js, or Go with automatic environment setup. It lists supported languages and features, and distinguishes from sibling tool 'shell' by stating to use shell for reusable scripts.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides explicit guidance on when to use (execute code snippets) and when not to (not for persistent scripts), with alternatives mentioned ('Use the shell tool to create reusable scripts'). It includes examples for each language.

    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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