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segalz

OpenCode Helper MCP

by segalz
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Server Quality Checklist

58%
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  • Latest release: v0.1.0

  • Disambiguation2/5

    The three tools share the same core operation—analyzing code at low reasoning effort—and differ only by backend model. Their descriptions include model names and a fallback order, but there is no distinct purpose-based choice; an agent can only distinguish them by which fallback step it happens to need.

    Naming Consistency5/5

    All tools follow the consistent pattern analyze_code_<model>, with matchings-style and no mixing of verbs or argument formats. The naming is predictable and readable.

    Tool Count4/5

    Three tools is not necessarily bloated, and the fallback approach justifies multiple endpooints. However, this functionality could just as easily be a single tool with model parameter, so the count is slightly inflated by the redundancy.

    Completeness3/5

    The server covers its narrow read-only code analysis goal by providing three fallback models. But beyond analyzing a snippet/no input, there is no capability for fetching code, changing analysis params, handling analysis output, or performing any related helper task, which leaves the surface limited.

  • Average 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
    • 14 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

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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 disclosure burden. It does convey 'read-only,' the fixed model, and low reasoning effort, which are meaningful behavioral details. It still lacks information about the tool's return format, error behavior, or failure modes, so transparency is incomplete.

    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 tightly written sentences with no filler. The core 'read-only code analysis' behavior is front-loaded, and the usage routing note follows efficiently.

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

    Completeness3/5

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

    For a small 2-parameter read-only tool, the description provides adequate context for purpose and routing. The absence of an output schema and the lack of any description of what the result looks like means the behavioral contract is not fully complete. Still, the read-only nature and model selection details mitigate many concerns.

    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 input schema already fully documents the two parameters. The tool description adds no parameter-level detail, which is acceptable at the baseline of 3 but no more.

    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 the tool performs read-only code analysis using a specific model, which goes beyond just restating the name. However, it does not explicitly distinguish itself from the Cloudflare sibling beyond the shared 'analyze_code' prefix, so differentiation is incomplete.

    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?

    It explicitly says 'Use this after MiMo fails or is insufficient,' giving a clear when-to-use condition and naming one alternative. It does not mention the Cloudflare sibling, so the routing guidance is partial but still useful.

    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 provided, the description carries the full burden, and it succeeds in an important way: 'Read-only' discloses the safety profile, while 'fixed model' and 'low reasoning effort' set expectations about determinism and output quality. It does not describe output format or potential rate limits, but for a read-only analysis tool this is reasonable.

    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?

    Two short sentences, zero filler. The core behavior is front-loaded and the usage directive is immediate. Every word earns its place.

    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 two-parameter read-only tool, the description gives model identity, safety profile, reasoning effort, and default usage priority. It is slightly thin on what the response looks like, but with 100% schema coverage and no output complexity this is acceptable.

    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 parameter meanings are already fully documented in the schema. The description adds no further param detail, so the baseline 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 states this is a read-only code analysis tool and identifies the exact model (Cloudflare Workers AI GLM 4.7 Flash). The phrase 'Read-only code analysis' gives a specific verb and resource, and the tool is differentiated from its siblings by being the recommended Cloudflare/GLM option.

    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?

    'Use this first' provides explicit selection guidance, telling the agent this is the default among sibling analysis tools. It does not explain when to prefer analyze_code_mimo or analyze_code_nemotron, but the priority is clearly established.

    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?

    Anontations are absent, and description carries the full burden. It discloses the important read-only nature of the operation and the low reasoning effort, a useful performance trait. It does not go into return maps or rate limits, but it does offer meaningful behavioral context beyond the bare schema.

    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 word I count is minimal, each sentence earns its place: the first defines the capability, the second defines the usage condition. The description is appropriately small, well-ordered, and holds no fluff.

    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 only two well-sdhema'd parameters and no output schema, the description does not need to explain internals. However, some behavioral aspects such as return semantics or output format are not touched, which are minor for a read-analy analysis tool.

    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?

    He schema already has 100% coverage for both required parameters: 'prompt' has a focused question description and 'directory' a path description. The description adds no new semantic detail, so a baseline 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 communicates a specific verb (analyze), a focused resource (code), and a specific model (fixed OpenCode MiMo V2.5 Free) with clarity and concision. By mentioning 'read-only' and naming Cloudflare GLM, it distinguishes itself from its primary sibling and provides a clear differentiation.

    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?

    It tells the agent exactly when to use it — after Cloudflare GLM fails or is insufficient — thereby providing a critical routing context. It does not explicitly mention the Nemotron sibling or exclude that alternative, which is a minor omission; hence the 4 rather than 5.

    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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