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zesun33

mcp-spice

by zesun33
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Server Quality Checklist

58%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    spice_run executes a simulation and returns results, while spice_toolchain_info reports environment/version details. Their purposes are completely distinct with no functional overlap.

    Naming Consistency4/5

    Both tools share the spice_ prefix and snake_case style, but spice_run uses a verb while spice_toolchain_info is more noun-like. This is a minor inconsistency rather than a chaotic naming scheme.

    Tool Count4/5

    With only two tools, the set is slightly below the typical 3-15 range, but it is still reasonable for a narrowly focused SPICE execution server. The small count matches the minimal scope.

    Completeness4/5

    The server covers the core run-and-inspect workflow with parsed .meas results and log tail access. It lacks raw output or full-log retrieval, but these are not obvious dead ends for the intended simple simulation use case.

  • Average 4/5 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
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

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  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

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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 must carry the full burden of disclosing behavior. It states that the tool returns runtime and version information, implying a read-only, non-destructive operation, but it does not explicitly confirm the absence of side effects or describe any potential errors or edge cases.

    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 concise sentence with no redundant or extraneous information. It directly states what is returned, making it efficient and well-structured.

    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 simple parameterless tool, the description covers the core need: it specifies what the tool returns (runtime and version). It does not detail the exact output format, but given the lack of input parameters and the straightforward nature of the tool, this is sufficient for most use cases.

    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 tool has zero parameters, and the description correctly includes no parameter details. Since there are no parameters to document, the baseline score of 4 applies.

    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's function with a specific verb ('Returns') and identifies the exact resources ('active container/host runtime' and 'ngspice version string'). It also implicitly distinguishes itself from the sibling tool 'spice_run' by being informational rather than execution-oriented.

    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 does not explicitly state when to use this tool versus alternatives, nor does it mention any prerequisites or situations where it should be avoided. The purpose is clear, but usage guidance is indirect at best.

    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?

    With no annotations, the description carries the full transparency burden. It clearly states the execution mode (batch), the return value (parsed .meas + log tail), and what it does not do (plot). This is transparent about its behavior, though it does not mention potential side effects like file writes or environment changes.

    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 concise sentences with no redundant information. It front-loads the core action and includes the key caveat about plotting and the sibling tool, making it efficient and well-structured.

    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?

    The description gives a reasonable sense of the output (parsed .meas results and short log tail) and points to the alternative for engine probing. While it does not elaborate on the format of .meas results or the contents of the log tail, it is sufficient for an agent to gauge whether this tool is appropriate without being overly verbose.

    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 schema already provides descriptions for all three parameters (cwd, netlist, timeout_ms), so the description adds little beyond reinforcing that netlist is a .cir/.sp path and timeout is in milliseconds. This is baseline coverage; the description does not significantly enhance parameter understanding beyond the 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 the tool runs a SPICE netlist in ngspice batch mode and returns parsed .meas results plus a log tail, with a specific verb and resource. It also distinguishes itself by explicitly noting it does not plot, which helps disambiguate from other tools.

    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 explicitly mentions the alternative tool spice_toolchain_info for probing the engine, and states what this tool does not do (plot). This gives the agent a clear cue on when to use this tool versus the sibling, though it could be more explicit about scenario-based selection.

    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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