Skip to main content
Glama
VGFP

colyseus-docs-mcp

by VGFP
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Local

Server Quality Checklist

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

  • Latest release: v0.17.10-mcp.1

  • Disambiguation5/5

    Each tool has a completely distinct purpose: listing all pages, reading a specific page, and searching across pages. There is no ambiguity or overlap.

    Naming Consistency5/5

    All three tool names follow a consistent verb_noun pattern using snake_case (list_docs, read_doc, search_docs), making them predictable and easy to understand.

    Tool Count4/5

    Three tools is minimal but appropriate for a documentation server, covering the essential operations of discovering, reading, and searching content. It is slightly on the thin side but well-scoped.

    Completeness4/5

    The tool surface covers the core documentation tasks (listing, reading, searching). Minor gaps exist, such as no way to get a table of contents or hierarchical navigation, but list_docs already provides category information, so the gaps are not critical.

  • Average 4.5/5 across 3 of 3 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 6 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is failing

  • 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.

    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

  • Behavior4/5

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

    The description discloses ranking behavior (weighted by title, headings, body) and that each hit includes a snippet. No annotations are provided, so the description carries the burden; it covers key behaviors but does not mention safety (e.g., read-only nature) or edge cases like no results.

    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, with two sentences plus a list of examples. Every sentence serves a purpose: core function, behavior, and usage guidance. No fluff or redundancy.

    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 search tool with 2 parameters and no output schema, the description provides sufficient context: scope, ranking, snippets, and example queries. It lacks coverage of response details when no results, but overall is complete enough for an AI agent to use correctly.

    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?

    Schema coverage is 100% with both parameters described. The description adds value by explaining that multi-word queries match pages containing every term and providing examples of effective queries (e.g., 'reconnection', 'schema @type'), going beyond schema details.

    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 it performs full-text search across all Colyseus documentation pages. It specifies the resource ('all Colyseus documentation pages') and the action ('search'), and distinguishes from siblings 'list_docs' and 'read_doc' by focusing on searching rather than listing or reading.

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

    Usage Guidelines4/5

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

    The description provides clear guidance on when to use the tool: 'Use this to find pages about a specific API, concept, or recipe' with relevant examples. However, it does not explicitly state when not to use it or mention alternatives like 'list_docs' or 'read_doc', leaving some room for ambiguity.

    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?

    No annotations provided, so description carries full burden. Describes output format: pre-processed Markdown with JSX stripped but code blocks and prose preserved. Implicitly indicates read-only operation. Does not disclose all possible behaviors (e.g., error handling, rate limits), but sufficient for scope.

    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?

    Three sentences with no fluff. First sentence states purpose, second gives guidance, third explains output. Efficiently front-loads key 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?

    No output schema, so description compensates by explaining return format. Provides usage guidance and sibling tool reference. Could mention error cases or page existence handling, but overall complete for a simple read tool with one parameter.

    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?

    Single parameter 'slug' has schema description already, but tool description adds valuable examples and notes that empty string or 'index' returns landing page. Schema coverage is 100%, and description enhances understanding beyond schema.

    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?

    Description clearly states verb and resource: 'Read a single Colyseus documentation page by its slug'. Examples of slugs are given. Distinguishes from sibling tools by referencing list_docs for discovery.

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

    Usage Guidelines4/5

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

    Explicitly advises to use list_docs first to discover available slugs. Provides context on when to use this tool (reading a specific page) versus listing all pages. Lacks explicit when-not-to-use or alternative guidance, but adequate given sibling context.

    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 full burden. It discloses return fields (slug, title, category, one-line description) and notes that slug is used for read_doc. It could mention ordering or pagination, but for a parameterless list tool, the transparency is good.

    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 paragraph of three sentences, each earning its place: main action, details of return value, usage guidance. It is front-loaded and free of fluff.

    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?

    Given no parameters, no output schema, and the presence of siblings read_doc and search_docs, the description is complete enough. It covers what the tool does, what it returns, and how to use the results. Minor gaps (e.g., error handling, sorting) are acceptable for a simple list.

    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?

    No parameters exist, so schema coverage is 100%. The description adds value by explaining that the slug field is meant to be used as the slug argument to read_doc, beyond just listing fields.

    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 'List every Colyseus documentation page available to this MCP server,' specifying the verb (list), resource (documentation pages), and scope (every page). It distinguishes from siblings read_doc (reads a specific page) and search_docs (searches).

    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 explicitly says 'Use this first to discover what topics are covered before reading individual pages,' providing clear when-to-use guidance and implicitly contrasting with read_doc and search_docs.

    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

colyseus-docs-mcp MCP server

Copy to your README.md:

Score Badge

colyseus-docs-mcp 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/VGFP/colyseus-docs-mcp'

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