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pwesolowski

Pioter MCP Server

by pwesolowski
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Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation3/5

    The two tools have overlapping purposes as both provide guidance for technologies, which could cause confusion. 'refactor_advice' focuses on code improvement, while 'technology_best_practices' covers broader practices, but the descriptions are vague enough that an agent might misselect when seeking general advice. Some overlap exists, but the distinct keywords 'refactor' and 'best practices' help differentiate them.

    Naming Consistency5/5

    Both tool names follow a consistent snake_case pattern with a clear noun_verb structure (e.g., 'refactor_advice', 'technology_best_practices'). There are no deviations or mixed conventions, making the naming predictable and readable throughout the set.

    Tool Count2/5

    With only 2 tools, the server feels thin and under-scoped for a general-purpose 'Pioter MCP Server', suggesting it might not cover enough functionality. This low count could limit agent capabilities, as typical servers in this domain would offer more varied operations. It's borderline too few for effective use.

    Completeness2/5

    Given the inferred domain of technology guidance, there are significant gaps in the tool surface. The server lacks basic operations like searching, listing technologies, or providing examples, and there's no coverage for related tasks such as troubleshooting or implementation steps. This incompleteness will likely cause agent failures when handling broader queries.

  • Average 3.1/5 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

  • 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.

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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 the full burden of behavioral disclosure. It states the tool 'Get refactoring advice' but doesn't describe how it behaves: e.g., whether it's a read-only operation, if it requires authentication, rate limits, or what the output format might be. This leaves significant gaps in understanding the tool's traits beyond basic functionality.

    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: 'Get refactoring advice for a specific technology and query.' It's front-loaded with the core purpose, has zero waste, and is appropriately sized for a simple tool with two parameters. Every word earns its place.

    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?

    Given the tool's moderate complexity (2 required parameters, no output schema, no annotations), the description is minimally adequate. It covers the purpose but lacks behavioral details and usage guidelines. With no output schema, it doesn't explain return values, but for a query-based tool, this might be acceptable if the behavior were clearer. It meets the minimum viable standard with clear gaps.

    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 description coverage is 100%, with clear descriptions for both parameters, including an enum for 'technology'. The description adds no additional meaning beyond the schema, such as examples or constraints. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.

    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's purpose: 'Get refactoring advice for a specific technology and query.' It specifies the verb ('Get refactoring advice') and resource ('for a specific technology and query'), making the function unambiguous. However, it doesn't explicitly differentiate from the sibling tool 'technology_best_practices', which might cover similar ground, so it doesn't reach the highest score.

    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. It doesn't mention the sibling tool 'technology_best_practices' or any other contexts, prerequisites, or exclusions. Usage is implied by the purpose but lacks explicit direction, leaving the agent to infer based on the name and parameters 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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves information ('Get'), implying a read-only operation, but doesn't address other aspects like authentication needs, rate limits, error handling, or what the output format looks like (e.g., structured list, text). This leaves significant gaps for a tool with no output 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 description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is appropriately sized and front-loaded, making it easy for an agent to parse quickly.

    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?

    Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is minimally adequate. It clarifies the purpose but lacks details on behavioral traits, output format, and differentiation from siblings. Without annotations or output schema, more context would be beneficial to fully guide the agent.

    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 description coverage is 100%, with clear documentation for both parameters, including an enum for 'technology' and optionality for 'query'. The description adds minimal value beyond this, as it only echoes the schema's purpose without providing additional context like example queries or usage scenarios. This meets the baseline for high schema coverage.

    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's purpose with a specific verb ('Get') and resource ('best practices for a specific technology'), making it immediately understandable. However, it doesn't explicitly differentiate from the sibling tool 'refactor_advice', which might also provide technology-related guidance, so it doesn't reach the highest score.

    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 like 'refactor_advice'. It doesn't mention any prerequisites, exclusions, or contextual cues for selection, leaving the agent to infer usage based solely on the tool name and description.

    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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