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KevinLvLight

envmgr-mcp-server

by KevinLvLight

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool serves a distinct purpose: envmgr_list enumerates environments, envmgr_ssh executes commands, and envmgr_browser provides browser access credentials. The descriptions clearly separate these functions with no overlapping scope.

    Naming Consistency4/5

    All tools share the envmgr_ prefix, making the namespace clear. The second part mixes a verb (list) with nouns (ssh, browser), which is a minor inconsistency, but the pattern remains predictable and readable.

    Tool Count5/5

    With only three tools, the server is tightly scoped to its purpose of providing environmental access. Each tool is necessary and the count is not excessive or inadequate for the domain.

    Completeness4/5

    The server covers the primary lifecycle of listing environments and accessing them via SSH or browser. Minor gaps such as environment add/remove or update exist, but these are likely managed externally via config files, so the surface is sufficient for its intended use.

  • Average 4.2/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 5 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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    {
      "$schema": "https://glama.ai/mcp/schemas/server.json",
      "maintainers": [
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      ]
    }

    Then . Browse examples.

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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?

    The description adds valuable context beyond annotations: it notes commands run as root and warns to use destructive commands with caution, complementing the destructiveHint and readOnlyHint annotations. It also states the output is the command output, providing basic behavioral expectations. No contradiction with 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 concise and front-loaded with the primary action. The bulleted usage examples are useful but could be trimmed; still, each line contributes meaningful guidance. Overall, it is efficient and well-organized.

    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?

    For a straightforward tool with no output schema, the description covers the core details: what it does, common use cases, and a safety warning about root privileges. It lacks explicit mention of error handling or return format specifics, but given the simple nature, it is sufficiently 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 all parameters. The description reinforces the env parameter by mentioning '与 envmgr_list 返回的名称一致' (which also appears in the schema) and offers example commands, but adds no novel parameter-level 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 executes shell commands via SSH on a specified environment and returns command output. It explicitly differentiates from siblings (envmgr_list lists environments, envmgr_browser for browsing) by making the action and resource specific.

    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 usage scenarios (e.g., tail logs, check processes, df -h) that imply when this tool is appropriate. It does not explicitly state when-not-to-use or name alternatives, but the context and sibling names make the intended usage clear.

    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?

    Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds context by specifying the return fields (url, username, password) and the intended reuse in automation tools, which goes beyond the annotations without contradicting them.

    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 concise and front-loaded with a one-sentence summary, followed by a succinct numbered usage flow. Each sentence contributes to understanding the tool's purpose and usage, with no 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?

    With one parameter, strong annotations, and no output schema, the description covers the return values and how to use them, which is sufficient for an agent to invoke the tool. Minor gaps like error handling for invalid env names exist but do not undermine completeness.

    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% for the single 'env' parameter, which already explains that it must match an environment name from envmgr_list. The description does not add any extra parameter meanings, aligning with the baseline 3 for high 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 explicitly states 'returns browser access URL and authentication credentials for the specified environment,' which is a specific verb+resource pairing. It also distinguishes itself from siblings (envmgr_list, envmgr_ssh) by focusing on browser automation access and usage flow.

    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 includes a clear usage flow: call this tool to get URL/username/password, then use browser automation tools like Playwright. It implies the intended context but does not explicitly contrast with alternatives (e.g., 'use envmgr_ssh for SSH access'), so it falls short of a 5.

    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?

    Beyond the annotations (readOnly, idempotent, non-destructive), the description adds valuable context: the config file path, the ability to override via ENVMGR_CONFIG, and the explicit statement that passwords are not returned. These details disclose behavioral traits not captured in annotations, and there is no contradiction.

    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 concise and well-structured: the first sentence states the purpose, and the second provides configuration path, return fields, and an important exclusion (passwords). Every sentence adds value, and the content is front-loaded with the core functionality.

    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?

    For a simple list operation with one optional filter and robust annotations, the description is complete. It specifies the exact returned fields, the configuration path, and the password exclusion, compensating for the lack of an output schema. No missing critical information for an agent to invoke the tool correctly.

    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?

    The only parameter 'type' has a complete description in the schema ('按环境类型过滤,不传则返回所有'), providing full coverage. The main description does not discuss the parameter, but since schema coverage is 100%, the 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?

    The description clearly uses the verb '列出' (list) with the resource 'environments', and even enumerates the returned fields (name, type, description, SSH status, browser status). This unambiguously distinguishes it from sibling tools like envmgr_ssh and envmgr_browser, which perform connection actions rather than listing.

    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 for viewing configured environments but does not explicitly state when to use this tool versus envmgr_ssh or envmgr_browser. There is no mention of alternatives, exclusion criteria, or specific scenarios, so the guidance remains implicit.

    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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  • Evaluate tool definition quality.

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