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betmoar

FastApply MCP Server

by betmoar
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

50%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: call_tool handles tool execution with safety features, while list_tools provides metadata about available tools. There is no overlap in functionality, making them easily distinguishable.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (call_tool, list_tools) with clear, descriptive names. The naming convention is uniform throughout the set, making it predictable and easy to understand.

    Tool Count2/5

    With only 2 tools, this server feels under-scoped for a general-purpose 'FastApply MCP Server'. The name suggests broader functionality, but the minimal tool set may limit agent capabilities in handling diverse tasks, indicating a potential mismatch with the implied scope.

    Completeness2/5

    Given the server name 'FastApply MCP Server', which implies application or deployment-related operations, the tool set is severely incomplete. It lacks core operations like applying configurations, managing resources, or handling deployments, leaving significant gaps for the domain it appears to target.

  • Average 2.7/5 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

  • 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 carries full burden for behavioral disclosure. It mentions 'robust safety checks' which hints at some safety considerations, but doesn't specify what these checks entail, what permissions are required, whether the operation is read-only or mutative, or what happens on failure. The 'unified branching' phrase is too abstract to provide meaningful behavioral context.

    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 appropriately concise at one sentence, but it's not front-loaded with clear purpose. While efficient in length, the abstract terminology ('unified branching,' 'robust safety checks') doesn't earn its place by providing actionable information. It's concise but not effectively structured for clarity.

    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 apparent complexity (handling tool calls with branching and safety), the description is incomplete. While an output schema exists (which helps with return values), the description fails to explain the core operation, parameter usage, or behavioral characteristics. For a tool that presumably orchestrates other tool calls, this level of abstraction is insufficient for proper understanding.

    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?

    The schema has 0% description coverage, so the description must compensate for both parameters. It provides no information about what 'name' and 'arguments' represent, their expected formats, or how they relate to the tool's purpose. The description's abstract language doesn't add meaningful semantic context beyond what the bare schema already shows.

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

    Purpose2/5

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

    The description states 'Handle tool calls with unified branching and robust safety checks,' which is vague and tautological - it essentially restates the tool name 'call_tool' with abstract modifiers. It doesn't specify what resource is being acted upon or what concrete operation occurs. Compared to sibling 'list_tools,' it fails to distinguish itself with a clear verb+resource combination.

    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. The description mentions 'unified branching' and 'safety checks,' but these are abstract concepts that don't translate to practical usage scenarios. There's no mention of prerequisites, when-not-to-use conditions, or how this differs from the sibling 'list_tools' tool.

    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. It states the tool returns metadata, but lacks details on behavioral traits such as response format, pagination, rate limits, or error handling. The mention of 'unified mode' adds some context but is vague.

    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 no wasted words. It is front-loaded with the core purpose, making it easy to understand 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 has 0 parameters, 100% schema coverage, and an output schema exists, the description is minimally adequate. However, it lacks details on behavioral aspects like what 'metadata' includes or how 'unified mode' affects the output, which could be important for an agent.

    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 0 parameters, and the schema description coverage is 100%, so no parameter documentation is needed. The description doesn't add parameter semantics, but this is acceptable given the lack of parameters, aligning with the baseline for 0 parameters.

    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 action ('Return') and resource ('metadata for all exposed tools'), specifying the scope with '(unified mode)'. It distinguishes from the sibling 'call_tool' by focusing on listing rather than invoking tools, though it doesn't explicitly mention this distinction.

    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. The description implies it's for retrieving tool metadata, but there's no mention of prerequisites, context, or comparison with the sibling 'call_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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