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TAO-MINGYU

Codex to OpenCode MCP Server

by TAO-MINGYU
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Python
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Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation4/5

    The two tools have clearly distinct roles: opencode_execute delegates a coding task, while opencode_check resolves the executable and reports its version. There is no realistic confusion between performing an action and checking the installation.

    Naming Consistency5/5

    Both tools use the same opencode_ prefix followed by a concise imperative verb, maintaining a consistent snake_case pattern. Though the prefix is a product name, the convention is uniform and predictable.

    Tool Count3/5

    Two tools is at the low end of typical server scope and feels thin for a coding integration. However, the narrow focus on delegating a single task plus a health check is reasonable enough to be borderline appropriate.

    Completeness4/5

    The server covers its core purpose: opencode_execute handles task delegation, and opencode_check verifies the underlying executable. Obvious lifecycle tools such as cancellation or querying current task status are absent, but they fall outside the stated minimal purpose.

  • Average 4.2/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 2 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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Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

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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

  • Behavior4/5

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

    The description adds meaningful behavior over the annotations (destructiveHint=true, readOnlyHint=false) by disclosing non-interactive execution, possible file modifications, CLI/Git actions, and execution of tests. It also warns that the returned Git diff is a post-run snapshot and may include pre-existing changes. This is useful, though it does not enumerate every destructive action or limitation.

    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 focused: one purpose-driven opening sentence followed by a terse but information-dense paragraph about behavior and result shape. Every sentence earns its place, including the important Git-diff caveat. The length is appropriate for the tool's complexity.

    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 a complete input schema and an output schema present, the description covers the main operational ambiguity: non-interactive execution, mutation of files, and the non-isolated nature of the Git diff. It is mostly complete, but it leaves the distinction versus opencode_check unstated, and it does not describe interaction between the timeout/model parameters and the result.

    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%, so the parameter descriptions in the schema already carry the meaning for task, working_directory, model, timeout, and reasoning_effort. The tool description does not add extra parameter syntax, examples, or semantic clarifications beyond what the schema provides.

    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 immediately states a specific operation: delegating one repository coding task to OpenCode, and enumerates what the tool can do (read/modify files, run commands, tests, Git). It is clear, but it does not explicitly contrast with the sibling tool opencode_check, so it stops short of full differentiation.

    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 first sentence implies the usage: delegate a coding task to OpenCode. The description also clarifies that it runs non-interactively in a repository, which helps the agent set expectations. However, there is no explicit 'when not to use' or comparison with the sibling opencode_check; usage guidance is present but largely implicit.

    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, idempotentHint, and destructiveHint, covering the safety profile. The description adds behavioral detail beyond those annotations by stating the tool resolves a configured executable, which implies reading configuration and locating a binary. 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.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    A single, front-loaded sentence states the key action and outcome, with no filler or redundant detail. Every word earns its space.

    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?

    The tool has no parameters, a complete annotation set, and an output schema, so the description only needs to convey the high-level purpose and behavior. It does so completely for this simple check operation.

    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?

    There are zero parameters, so the baseline is 4. The description does not need to explain any parameters, and the schema fully covers them.

    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 uses a specific verb-resource pair: 'Resolve the configured OpenCode executable and report its version.' It clearly identifies both the action and the subject, and the title reinforces the purpose. It is also easily distinguishable from the sibling tool opencode_execute, which implies running commands rather than checking the installation.

    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 a clear context: this is a verification or preflight check of the local OpenCode installation, distinct from execution. While it does not explicitly cite when-or-when-not to use it relative to opencode_execute, the 'check' framing and resolved-executable behavior provide clear usage context without exclusions.

    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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