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MauricioPerera

subway-mcp-server

by MauricioPerera
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Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation2/5

    The three tools are nearly identical in description and parameters, differing only in the target CLI. An agent would struggle to choose between them without additional context about each CLI's capabilities.

    Naming Consistency5/5

    All tools follow the consistent pattern 'delegate_to_<name>', making it clear they are delegation actions to specific CLIs.

    Tool Count5/5

    Three tools is appropriate for a server focused on delegating to a specific set of AI CLIs. The count is within the typical well-scoped range.

    Completeness3/5

    The server covers three major AI CLIs, but lacks tools for listing available CLIs, models, or managing runs, which leaves minor gaps for agent workflows.

  • Average 4.2/5 across 3 of 3 tools scored. Lowest: 3.6/5.

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

    • No community issues in the last 6 months
    • 7 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.

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  • 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?

    Annotations indicate destructiveHint=true and openWorldHint=true, but the description adds critical details: non-interactive mode, auto_approve necessity (tasks fail without terminal), timeout killing, and structured return values. This significantly enriches understanding beyond annotations.

    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 clear sections (Args, Returns, Error Handling). Every sentence contributes practical information, though some default values are repeated between schema and description, making it slightly longer than necessary.

    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 (5 parameters, output schema), the description covers input semantics, return structure, and error handling comprehensively. No critical gaps are evident, though concurrency or resource impacts are not addressed.

    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?

    With 100% schema coverage, the description still adds value by explaining defaults (cwd, model), behavioral implications (auto_approve), and range constraints (timeout). This context helps the agent craft appropriate parameter values.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states it runs Codex CLI non-interactively to execute a coding task and returns results. While it doesn't explicitly differentiate from siblings (delegate_to_agy, delegate_to_claude), the title and specificity to Codex make the purpose unambiguous.

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

    Usage Guidelines2/5

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

    The description provides no guidance on when to use this tool vs. its siblings. It neither explains the distinguishing characteristics of Codex CLI nor suggests alternatives for other AI agents, leaving the agent to infer usage from the name alone.

    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?

    Adds substantial behavioral details beyond annotations: describes process killing on timeout, structured return value format, and error handling (success, exit_code, timed_out, stderr). Annotations indicate destructiveHint=true, and the description warns of side effects via auto_approve. 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?

    Well-structured with sections for args, return format, and error handling. Every sentence is informative and earns its place. 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 full schema coverage, output schema, and annotations, the description is highly complete. Covers purpose, usage caveats, return values, error states, and parameter details comprehensively.

    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% with good descriptions for each parameter. The description does not add significant new meaning beyond summarizing the schema. Baseline 3 is appropriate.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    Clearly states it runs Claude Code CLI non-interactively to execute a task and returns the final message. Includes the exact CLI invocation format. Names sibling tools but does not explicitly contrast them, so slightly below top tier.

    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?

    Explains the non-interactive context and warns that auto_approve is needed for tool-using tasks (otherwise they fail). Provides implicit when-to-use guidance for unattended execution. Lacks explicit when-not-to-use or comparison with alternatives.

    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?

    The description discloses output format, error handling, timeout behavior, and the necessity of auto_approve for tool-using tasks. This goes well beyond the annotations, which are already present.

    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 a summary line, parameter listing, return format, and error handling. It is concise and front-loaded with the essential purpose.

    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 (5 parameters, output schema, annotations), the description covers return values, error cases, and operational nuances, making it fully complete.

    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 coverage is 100%, but the description adds valuable context for parameters like auto_approve (explaining failure without it) and timeout (kill behavior), exceeding the baseline of 3.

    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 runs the Antigravity CLI non-interactively to execute a task, using a specific verb and resource. The title and description distinguish it from sibling tools by naming the specific CLI.

    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 implies when to use (delegating to Antigravity CLI) and highlights the need for auto_approve, but does not explicitly compare with siblings or state when not to use it.

    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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