Skip to main content
Glama
nullmastermind

mcp-feedback-enhanced-gw

by nullmastermind
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Hybrid

Server Quality Checklist

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

  • Latest release: v2.6.2

  • Disambiguation5/5

    The two tools have completely unrelated purposes: one handles interactive feedback collection, the other retrieves system information. There is no overlap or confusion between them.

    Naming Consistency4/5

    Both names use snake_case, but 'interactive_feedback' uses an adjective+noun pattern while 'get_system_info' follows verb+noun. This minor inconsistency prevents a perfect score.

    Tool Count2/5

    With only two tools, the server feels underdeveloped. The name suggests a feedback-focused gateway, yet only one tool is feedback-related and the other is unrelated system info.

    Completeness2/5

    The feedback tool is described extensively but there is no explicit end tool, retrieval of prior feedback, or integration with the system info tool. The overall domain coverage is sparse and ad hoc.

  • Average 3.8/5 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
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

    MCP servers without a LICENSE cannot be installed.

  • 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.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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?

    With no annotations provided, the description must fully disclose behavioral traits. It mentions that the return value is a JSON string, but it omits whether the operation is read-only, any required permissions, or potential side effects. This lack of disclosure is a notable gap for a tool with zero annotation coverage.

    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 succinct and front-loaded, with the core purpose stated first and return details following. It contains no extraneous content and is appropriately sized for a simple, no-argument tool.

    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?

    For a tool with no parameters and an output schema, the description covers the essential information: it retrieves system info and returns it as JSON. It does not specify the exact fields or scope of 'system information', but the output schema likely provides that detail. A brief mention of data scope or usage context would make it more complete, but it is largely sufficient for its simplicity.

    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?

    The tool has zero parameters, which sets a baseline of 4. The description implicitly confirms that no inputs are needed, and the empty schema corroborates this. No additional parameter semantics are required.

    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 resource. It is well-differentiated from the sibling tool 'interactive_feedback', which serves a completely different purpose. Even though the name already implies this, the added return format detail strengthens 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?

    The description provides no guidance on when to use this tool versus alternatives, such as conditions, prerequisites, or comparisons with 'interactive_feedback'. It simply states what it does, leaving the agent without context for selecting this tool over others.

    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?

    With no annotations, the description carries the full burden of disclosure. It reveals key behavioral traits: the tool must be called repeatedly, waits for user feedback with a timeout, and requires the agent to adjust based on feedback. It also instructs to provide the project directory for user awareness. However, it does not specify what happens when the timeout expires or the exact output format, though an output schema exists.

    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?

    The description is well-structured with a heading and numbered rules, but it contains redundancy: rules 1 and 2 both mandate calling the tool frequently, and rule 3 repeats the need to call again after feedback. It could be more concise while retaining the same information.

    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?

    For a tool with three simple parameters and an output schema present, the description is largely complete. It thoroughly explains when and how to use the tool, what to include, and termination conditions. Minor gaps include lack of detail on timeout expiry behavior, but overall it provides sufficient context for an agent to use the tool correctly.

    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%, meaning all three parameters (summary, timeout, project_directory) are already documented in the schema. The description adds minimal extra meaning by explicitly stating that project_directory should be provided so the user knows what was done, but it does not enrich the semantics of summary or timeout beyond their schema descriptions.

    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 identifies the tool as an interactive feedback collection tool for LLM agents, with a specific purpose of soliciting and adjusting based on user feedback. It distinguishes from the sibling tool get_system_info by focusing on feedback collection rather than system information.

    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 explicit 'USAGE RULES' with detailed directives: when to call (during any process, after receiving feedback), when to stop (when user says 'end' or 'no more interaction needed'), and what to include (summary and project_directory). This is exceptional guidance that leaves little ambiguity for the agent.

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

GitHub Badge

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.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

mcp-feedback-enhanced MCP server

Copy to your README.md:

Score Badge

mcp-feedback-enhanced MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/nullmastermind/mcp-feedback-enhanced'

If you have feedback or need assistance with the MCP directory API, please join our Discord server