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henrydontbbai

OpenCode Advisor MCP

by henrydontbbai
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Server Quality Checklist

58%
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  • Latest release: v0.3.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: ask_opencode_advisor reviews git changes, ask_opencode_planner improves plans, and get_opencode_task checks task status. No overlap.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern in snake_case: ask_opencode_advisor, ask_opencode_planner, get_opencode_task.

    Tool Count3/5

    With only 3 tools, the server is minimal but acceptable for a focused local agent interaction. However, it borders on too few for a complete agent workflow.

    Completeness3/5

    The server provides tools to ask agents and check task status, but lacks tools to retrieve results, cancel tasks, or configure agents, creating potential dead ends.

  • Average 2.8/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 78 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • 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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  ]
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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

  • Behavior2/5

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

    Without annotations, the description must disclose behavioral traits; it states 'read-only' and 'without taking over implementation', which hints at safety, but does not detail side effects, required permissions, or operational constraints for a tool with 10 parameters.

    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 single-sentence description is very concise and front-loaded with the key purpose, but it may be too terse given the tool's complexity.

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

    Completeness1/5

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

    Given the high parameter count (10), lack of output schema, and no annotations, the description is far too brief to provide sufficient context for correct invocation and understanding of the tool's behavior.

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

    Parameters1/5

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

    Schema description coverage is 0%, and the description provides no information about any of the 10 parameters (cwd, goal, paths, etc.), failing to add meaning beyond their names and types.

    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 is for asking a planning partner to improve a plan, distinguishing it from siblings (advisor, task) by specifying it's a planning partner that is read-only and doesn't implement.

    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 implies usage for improving plans without implementation, but provides no explicit when-to-use, when-not-to-use, or alternatives compared to sibling tools.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior2/5

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

    The description mentions 'read-only', implying no destructive side effects, but provides no further behavioral details such as output format, required dependencies (e.g., git repo), or error conditions. With no annotations, this is insufficient for a tool with 8 parameters.

    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 a single sentence, which is concise and to the point. However, it could benefit from additional structure (e.g., listing what the advisor does) without becoming overly verbose.

    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?

    Given 8 parameters, no output schema, and no annotations, the description is far too minimal. It does not explain the review process, expected output, or parameter interactions, making it hard for an agent to use correctly without prior knowledge.

    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?

    Schema description coverage is 0%, yet the description adds no explanation for any of the 8 parameters. The parameter names give some hints, but the description does not clarify their types, constraints, or when to use each, leaving ambiguity.

    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 asks a read-only advisor to review current git changes. It distinguishes from siblings like ask_opencode_planner and get_opencode_task, which focus on planning and task retrieval respectively.

    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 guidance is given on when to use this tool versus alternatives. The description lacks any 'when to use' or 'when not to use' context, leaving the agent to infer based on the name and description alone.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior2/5

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

    No annotations are provided, so the description carries full burden. It states the tool checks completion but does not disclose behavior on non-existent tasks, errors, or return format. Minimal behavioral disclosure.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

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

    A single 14-word sentence is front-loaded but too brief. It leaves out parameter explanation that could fit without being verbose.

    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?

    No output schema and no description of return values. For a tool that polls status, the description should hint at expected response or how to interpret 'finished.' Incomplete for its complexity.

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

    Parameters1/5

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

    Schema description coverage is 0% and the description does not explain what task_id is or how to obtain it. The description adds no meaning beyond the schema's raw type definition.

    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 verb 'Check' and the resource 'whether a queued or running OpenCode planner/reviewer task has finished,' distinguishing it from sibling tools like ask_opencode_advisor and ask_opencode_planner, which are for different actions.

    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 usage after submitting a task via sibling tools, but does not explicitly state when not to use or provide alternatives. It is clear enough for a simple polling tool.

    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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