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teacat99

gl-mcp-feedback

by teacat99
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Hybrid

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v2.2.1

  • Disambiguation5/5

    The two tools have clearly distinct purposes: `get_system_info` retrieves environment information, while `interactive_feedback` collects user feedback via a Web UI. There is no overlap or potential for confusion between them.

    Naming Consistency5/5

    Both tools follow a consistent `verb_noun` pattern with snake_case: `get_system_info` and `interactive_feedback`. The naming style is uniform and predictable.

    Tool Count3/5

    With only two tools, the server feels minimal. While the `interactive_feedback` tool is heavily featured, the overall surface may be too narrow for a general-purpose feedback server. A few more utility tools (e.g., `store_feedback`, `configure`) could improve scope.

    Completeness4/5

    For its stated purpose (collecting user feedback), the tool set is fairly complete. `interactive_feedback` handles all communication and feedback, and `get_system_info` provides context. There is a minor gap in persistence or configuration, but core workflows are covered.

  • Average 4.3/5 across 2 of 2 tools scored. Lowest: 3.7/5.

    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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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?

    No annotations are provided, so the description must carry the burden. It states the return type (str in JSON format) but does not disclose any behavioral traits like side effects, permissions, or rate limits. Since it is a simple getter, the lack of negative traits is mildly handled.

    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 extremely concise (two sentences) and front-loaded with the purpose. There is no wasted text; every word contributes to understanding the tool's function and output.

    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 has no parameters and an output schema exists, the description provides sufficient information to understand what the tool does and returns. However, it could be more explicit about the nature of the 'system environment information' and whether it includes sensitive data. Overall, it is minimally complete.

    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 no parameters, and the input schema is empty with 100% coverage. According to rules, baseline for 0 parameters is 4. The description adds no param info, but that is acceptable as there are none to describe.

    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 retrieves system environment information, using a specific verb 'get' and a resource 'system_info'. It is unambiguous and distinguishes from the sibling 'interactive_feedback' which serves a different purpose.

    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 usage guidelines are provided. The description does not indicate when to use this tool versus alternatives, nor does it mention any preconditions or context. The sibling tool is different but not compared.

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

  • Behavior5/5

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

    With no annotations provided, the description carries full burden and transparently discloses all behavioral traits: it opens a Web UI, expects user feedback, requires re-calling on skip/timeout, rejects empty strings and placeholder phrases, and validates schema strictly. It also explains return format (TextContent with feedback and images) and failure cases.

    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 long but well-structured with headings, numbered lists, and bold for emphasis. It front-loads the core purpose. While every sentence is informative, it could be more concise as some details (e.g., specific failure messages) are verbose. For the high information density, it earns a 4.

    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?

    Given the tool has 4 parameters and no output schema, the description covers all necessary context: return type (TextContent with feedback images), empty result meaning skip, runtime rejection of empty summary, and schema validation failure on unknown params. It is complete for an interactive feedback tool.

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

    Parameters5/5

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

    Schema coverage is 100%, yet the description adds significant value: detailed 'summary' writing rules (language, Markdown, what to include, what to avoid), 'title' writing rules (optional, ≤30 chars, examples), 'timeout' minimum (≥600), and a warning against passing 'question'/'choices' etc. It also notes that passing unknown param names causes schema validation failure.

    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 opens a Web UI for collecting interactive feedback, and positions it as the PRIMARY communication channel. It distinguishes from the sibling tool 'get_system_info' by its role. The verb 'open' and resource 'Web UI to collect interactive feedback' are specific and unambiguous.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides extensive, explicit when-to-use and when-not-to-use guidelines. It lists seven mandatory call scenarios, strict prohibitions (e.g., not using AskQuestion or ending with plain text), and a termination condition. It also names the alternative 'AskQuestion' and explicitly instructs to route questions through this tool instead.

    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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