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hotjoebq

MCP Stdio Server

by hotjoebq
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Server Quality Checklist

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  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: 'calculate' handles mathematical operations, while 'list_files' deals with file system navigation. There is no overlap or ambiguity between these functions, making tool selection straightforward for an agent.

    Naming Consistency3/5

    The naming is mixed: 'calculate' uses a verb-only format, while 'list_files' follows a verb_noun pattern. Although both are readable, the inconsistency in naming conventions (one tool lacking an object) prevents a higher score for consistency.

    Tool Count2/5

    With only two tools, the server feels thin and under-scoped for a 'Stdio Server' which typically implies broader utility or system operations. This minimal set may limit agent capabilities in handling diverse tasks, suggesting an incomplete or overly narrow implementation.

    Completeness2/5

    Given the server name 'MCP Stdio Server', which implies standard input/output or general system utilities, the toolset is severely incomplete. It lacks essential operations like reading/writing files, executing commands, or managing processes, creating significant gaps that will hinder agent workflows.

  • Average 2.8/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 0 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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  • 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

  • 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 mentions 'perform' but doesn't disclose behavioral traits like error handling, precision, supported operations, or output format. It's minimal and lacks necessary context for a tool with no 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?

    The description is a single, efficient sentence with zero waste. It's appropriately sized and front-loaded, making it easy to parse quickly.

    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 no annotations, no output schema, and a single parameter with full schema coverage, the description is incomplete. It doesn't explain return values, error cases, or behavioral context, leaving significant gaps for a tool that performs calculations.

    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 schema already documents the 'expression' parameter. The description adds no meaning beyond what the schema provides, such as examples of complex expressions or limitations. Baseline 3 is appropriate when schema does the heavy lifting.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose3/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description 'Perform mathematical calculations' states a general purpose but lacks specificity about what kind of calculations or resources are involved. It doesn't distinguish from the sibling 'list_files', but it's not tautological with the name 'calculate'.

    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 provides no guidance on when to use this tool versus alternatives or any contextual prerequisites. It's a standalone statement with no implied usage scenarios or exclusions.

    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 the full burden of behavioral disclosure. It states it's a list operation, implying read-only behavior, but doesn't mention any constraints like permissions, rate limits, pagination, or what happens with invalid paths. For a tool with zero annotation coverage, this leaves significant behavioral gaps.

    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, clear sentence with no wasted words. It's front-loaded with the core purpose, making it highly efficient and easy to parse.

    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 no annotations and no output schema, the description is incomplete for a tool that likely returns a list of files. It doesn't explain the return format, error conditions, or behavioral details, leaving the agent with insufficient context for reliable use.

    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%, with the parameter 'directory' fully documented in the schema. The description doesn't add any meaning beyond the schema, but the baseline is 3 when schema coverage is high, as the schema already provides adequate parameter information.

    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 verb ('List') and resource ('files in the project directory'), making the purpose immediately understandable. It doesn't distinguish from the sibling tool 'calculate', but that's a different function, so the lack of explicit differentiation doesn't significantly harm clarity.

    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 guidance is provided on when to use this tool versus alternatives or in what context it's appropriate. The description only states what it does, not when or why to invoke it, leaving the agent without usage context.

    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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  • 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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