Skip to main content
Glama
endmoseung

sociableWiki

by endmoseung
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Local

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a clear and distinct purpose: list_topics for overview, read_doc for reading full content, and search_knowledge for full-text search. There is no overlap or ambiguity.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern in snake_case (list_topics, read_doc, search_knowledge), making them predictable and easy to understand.

    Tool Count5/5

    With only 3 tools, the server is concise and well-scoped for a read-only knowledge base. Each tool serves a necessary function without redundancy or gaps.

    Completeness5/5

    The tool set covers the core needs of a knowledge base: browsing topics, reading full documents, and searching. For its stated purpose, there are no obvious missing operations.

  • Average 4/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 4 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

  • Behavior3/5

    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 discloses that the search is full-text, supports English and Korean, returns ranked matches with concept ids. However, it does not specify ranking criteria, case sensitivity, or wildcard support, leaving some behavioral aspects implicit.

    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 that pack in the core purpose, domain context, language support, return format, and a clear next step. Every sentence earns its place with no extraneous 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?

    Given the absence of an output schema, the description adequately explains that the tool returns ranked matches with concept ids and directs to read_doc for full text. However, it does not mention whether the tags parameter affects the search or how ranking works, but the schema covers tags. Overall, it is mostly complete for a search tool with good schema coverage.

    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%, so each parameter already has a description. The tool description does not add new parameter-level details beyond what is in the schema. The mention of language support aligns with the query parameter but does not enhance it. Baseline score of 3 is appropriate.

    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 performs full-text search over a specific knowledge base, specifying the domains (AI-native development, agent/harness engineering, dev practice). It distinguishes itself from siblings by explicitly indicating that results are ranked matches with concept ids and directing the user to call read_doc for full text, which differentiates from list_topics.

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

    Usage Guidelines3/5

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

    The description provides a clear next action (call read_doc with an id), but does not explicitly state when to use this tool versus its siblings (list_topics, read_doc). It implies that search is for finding relevant documents, but lacks explicit 'when to use' or 'when not to use' guidance.

    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?

    With no annotations, the description must fully disclose behavior. It states it reads the full doc and mentions language availability, but does not cover potential issues like doc size limits, authentication needs, or response format.

    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 two concise sentences: the first conveys the core purpose, the second adds crucial language guidance. No wasted words.

    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?

    The description is adequate for a simple read tool, but lacks details on return format (e.g., JSON structure) and error handling (e.g., missing ID). Given no output schema, these omissions reduce completeness.

    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%, but the description adds value by explaining the 'id' originates from sibling tools and that 'lang' defaults to 'en' with Korean variant available. This enriches the parametric understanding beyond the 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?

    The description clearly states the tool reads a full knowledge doc by concept id, specifying the verb 'read' and resource 'knowledge doc'. It distinguishes from siblings by indicating the source of the ID (list_topics, search_knowledge).

    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 context for when to use the tool: when you have a concept id from search_knowledge or list_topics. It also gives guidance on language parameter use. However, it lacks explicit exclusions or when-not-to-use conditions.

    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?

    With no annotations, the description must disclose behavioral traits. It explains the two possible outputs (human-curated or generated) but lacks details on side effects, permissions, or rate limits. For a read-only listing, this is adequate but not comprehensive.

    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 concise sentences with no redundancy. The critical information is front-loaded, and every phrase earns its place.

    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 zero parameters, no output schema, and no annotations, the description is fairly complete. It explains what the tool returns and suggests a use case, though it could mention output format limitations.

    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, so baseline is 4. The description adds value by explaining the output nature (curated vs generated), which helps the agent understand the result beyond the empty 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?

    The description clearly states the tool's purpose: providing an overview of wiki topics, either as a human-curated map or generated listing grouped by area. It effectively distinguishes from siblings (read_doc, search_knowledge) by positioning itself as an initial broad view.

    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?

    It explicitly suggests 'Good first call to see what's here,' indicating when to use it as a starting point. However, it does not explicitly exclude scenarios or reference alternatives, though sibling names imply better tools for specific tasks.

    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

sociableWiki MCP server

Copy to your README.md:

Score Badge

sociableWiki 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/endmoseung/sociableWiki'

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