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sundev126

netmiko-mcp

by sundev126
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

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

  • Latest release: v0.0.1

  • Disambiguation5/5

    list_devices handles inventory enumeration while run_command handles device execution. There is no overlap or ambiguity between the two operations.

    Naming Consistency5/5

    Both tools follow the same verb_noun snake_case pattern: list_devices and run_command. The naming is consistent and predictable.

    Tool Count3/5

    With only two tools, the server feels thin even though each tool serves a clear purpose. It is borderline for a focused SSH/network automation server.

    Completeness4/5

    The two tools cover the core workflow of enumerating devices and running commands in read or config mode. More advanced operations like inventory management or structured config retrieval are missing, but they are workable via run_command.

  • Average 3.6/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

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  • This repository includes a README.md file.

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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

  • Behavior3/5

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

    With no annotations, the description carries the full burden of behavioral disclosure. It does add meaningful context by explaining that mode can be read-only or config-push, which hints at safety differences. However, it does not disclose output format, side effects of config mode, permissions needed, or error behavior, leaving significant behavioral ambiguity.

    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 two short sentences with no filler. The primary action is front-loaded and the mode clarification is useful and directly relevant. Every word earns its place.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For a tool with three required parameters, no schema descriptions, no annotations, and no output schema, this description is too sparse. It fails to explain what target refers to, what format command should take, or what the tool returns. It is minimally viable but leaves important gaps for an agent to call it correctly.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The input schema provides no descriptions for any of the three required parameters, and the description only explains the meaning of mode (read/config). The target and command parameters are left entirely to inference. At 0% schema coverage, the description should compensate more heavily but does not.

    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 a command on a target device, which is a specific verb and resource. It also distinguishes itself from the sibling tool list_devices by focusing on command execution rather than device enumeration. The mode distinction adds further clarity to the purpose.

    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 when to use the tool—whenever a command needs to be executed on a device—and notes the read/config mode split. However, it provides no explicit guidance on when not to use it or when to prefer the sibling list_devices instead. The usage context is implied rather than explicit.

    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?

    Even though annotations are absent, the description explicitly discloses that passwords, secrets, and sudo passwords are excluded from results. This is a valuable behavioral caveat for a list operation and goes beyond what the name alone suggests.

    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 a single compact sentence with the key privacy caveat parenthesized. There is no filler, redundancy, or unnecessary detail.

    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 zero-parameter, no-output-schema tool, the description adequately captures what the tool does and an important constraint on its results. It could additionally state that it is read-only or describe the result format, but these are non-critical for selecting and invoking the tool.

    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 input schema has zero parameters, so parameter documentation is not applicable. The baseline of 4 is appropriate because there is nothing for the description to clarify.

    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 a specific verb ('enumerate') and resource ('target devices in the inventory'). It is semantically distinct from the sibling run_command, but it does not explicitly name or differentiate from that sibling.

    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?

    No explicit when-to-use or when-not-to-use guidance is provided. The sibling run_command is not mentioned, so the agent must infer the appropriate choice from tool names and intent rather than from explicit 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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