Skip to main content
Glama
gotalab

MCP Novel Game Server

by gotalab

Server Quality Checklist

50%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a distinct purpose: selecting a story, recording a choice, and loading a scene image. No overlap in functionality.

    Naming Consistency4/5

    All tool names use lowercase with underscores, but the structure varies: 'choose' is a single verb while others follow verb_noun pattern. The inconsistency is minor.

    Tool Count5/5

    With 3 tools, the server is well-scoped for a simple novel game interface. Each tool serves a clear purpose without unnecessary bloat.

    Completeness3/5

    The basic interaction flow is covered, but missing tools like fetching story list, scene text, or saving progress leave notable gaps that agents may need to work around.

  • Average 2.7/5 across 3 of 3 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, the description must disclose behavior, but it only says 'record'. It fails to mention if the action is idempotent, if it can be overwritten, or any side effects. This is insufficient for safe usage.

    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 a single sentence, making it concise, but it sacrifices necessary detail. Every word is needed, but additional context would improve it.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness1/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given no output schema or annotations, the description is severely incomplete. It does not explain return values, error conditions, or prerequisites like having an active story.

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

    Parameters1/5

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

    The schema has 0% description coverage, and the description adds no parameter details. It does not explain the role of player_id, current_scene_id, choice_id, or free_text beyond their names.

    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 records a player's choice within a selected story, distinguishing it from siblings like load_scene_image and select_story. However, it lacks explicit mention that the choice is tied to a specific scene, which is evident from the schema.

    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, such as selecting a story or loading an image. The agent is left to infer usage from the name alone.

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

  • Behavior3/5

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

    Adds one behavioral trait: resizing/compressing until data size <= 1MB. However, no annotations exist, so the description carries the full burden. It does not disclose authentication needs, rate limits, error handling, or behavior when compression fails.

    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?

    Two sentences with no wasted words. The core action is front-loaded. Every sentence adds value: purpose and behavioral constraint.

    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?

    No output schema, yet description does not specify return format (e.g., base64, URL) or error cases. For a tool with 4 parameters and no annotations, the description is incomplete. It covers basic purpose but omits many usage details.

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

    Parameters1/5

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

    Schema coverage is 0%, and the description provides no explanation of parameters. It mentions resizing/compression but does not link to max_width or max_height. The description fails to compensate for the lack of parameter descriptions in the schema.

    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 it returns an image for the player's current story and scene, with resizing/compression. The verb 'Return' and resource 'image for the player's current story and scene' are specific. While siblings are different actions, it doesn't explicitly differentiate or highlight its unique role.

    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 on when to use this tool versus alternatives. No mention of context, prerequisites, or exclusions. The description implies it should be used when an image is needed for a scene, but lacks explicit usage direction.

    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 must carry the full burden of behavioral disclosure. The description only states the action and return value but does not elaborate on side effects, failure modes, or required permissions (e.g., whether binding is permanent, what happens if the story is already bound, or if the player 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 a single sentence that earns its place by stating the core purpose and return value. However, it leans toward under-specification, lacking necessary details for a tool with no annotations or output schema. It is concise but not sufficiently informative.

    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 the lack of annotations, output schema, and parameter descriptions, the description is incomplete. It does not cover parameter semantics, usage context, or behavioral details, leaving significant gaps 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.

    Parameters2/5

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

    The input schema has 0% description coverage for its two parameters, and the description adds no additional meaning beyond the parameter names 'player_id' and 'story_id'. While the names are self-explanatory, the description fails to explain expected formats, constraints, or relationships between parameters.

    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 action 'Bind a story to the player' and the return 'its opening scene', using specific verbs and resources. It distinguishes itself from siblings 'choose' (likely for making choices) and 'load_scene_image' (loading an image) by focusing on binding a story and returning the opening scene.

    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 the siblings 'choose' or 'load_scene_image'. There is no context about prerequisites, typical use cases, or scenarios where alternative tools should be used instead.

    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-novel-game-server MCP server

Copy to your README.md:

Score Badge

mcp-novel-game-server 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/gotalab/mcp-novel-game-server'

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