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auditsocials

AuditSocials Glossary MCP

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by auditsocials

Server Quality Checklist

67%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a clearly distinct and non-overlapping purpose: define a specific term, search by keyword, and list categories. There is no ambiguity about which tool to use for a given task.

    Naming Consistency5/5

    All tools follow the consistent pattern 'glossary_<verb>_<noun>' (glossary_define, glossary_search, glossary_list_categories). The naming is predictable and clearly conveys the action and resource.

    Tool Count4/5

    Three tools is a reasonable number for a focused glossary server. It covers the core operations without being too sparse, though a few more tools (e.g., list all terms) could be added without causing bloat.

    Completeness4/5

    The tool surface covers the essential workflows: discovering terms (search and categories) and retrieving detailed definitions. A minor gap is the lack of a tool to list all terms directly, but the search and category tools adequately enable discovery.

  • Average 4.2/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 3 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
  • This repository is licensed under CC BY-4.0.

  • 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.

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    {
      "$schema": "https://glama.ai/mcp/schemas/server.json",
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      ]
    }

    Then . Browse examples.

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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?

    With no annotations provided, the description carries the full burden of disclosure. It states that the tool returns 'matching terms with their short definitions,' indicating a read-only search. It does not mention potential errors, pagination, or authentication needs, but for a simple search tool this is adequate.

    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: the first clearly states the action and result, the second provides usage guidance. Every word adds value with no redundancy or filler.

    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 3 well-documented parameters and an implied list return, the description adequately covers the basics. It explains the output and provides workflow context. It could briefly mention that results are a list of terms, but given the schema covers parameters, this is not a significant gap.

    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 coverage is 100% with all parameters already described in the schema. The description mentions 'keyword and/or category' but adds no new semantic details beyond what the schema provides, so a 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's function: searching the glossary by keyword and/or category and returning matching terms with definitions. It distinguishes itself from the sibling 'glossary_define' by noting this tool is for discovery before calling the define tool.

    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 explicitly tells when to use this tool: 'Use this to discover terms before calling glossary_define.' This provides clear guidance on the workflow, though it does not discuss when not to use it or mention the other sibling 'glossary_list_categories'.

    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 correctly indicates a read-only operation (listing with counts) and adds context about grouping by category. The behavior is transparent and appropriate, though no details on potential pagination or output format are given.

    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 sentences with no wasted words. It front-loads the core action and then explains utility, perfect for quick agent parsing.

    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 simple sibling context, the description is sufficiently complete. It explains the output (categories with counts) and its usefulness. Minor gap: no mention of sorting or hierarchical structure if applicable.

    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, so the description need not add parameter details. It compensates by explaining what information is returned (categories with term counts), adding meaning 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 lists glossary categories with term counts, using specific verbs ('list') and resources ('categories'). It distinguishes itself from siblings like glossary_define and glossary_search by highlighting the purpose of understanding vocabulary scope.

    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 implies usage by noting it's useful for understanding vocabulary scope, but provides no explicit guidance on when to use this versus siblings. It lacks exclusions or context for tool selection.

    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 are provided, so the description carries full burden. It clearly implies a read-only lookup (returns a definition) with no side effects. The description does not mention errors, auth, or rate limits, but for a simple glossary lookup, the behavioral traits are sufficiently transparent.

    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 sentences long, front-loaded with the core purpose, and contains no extraneous information. Every sentence adds value, making it highly efficient.

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

    Completeness5/5

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

    Given the tool's simplicity (single parameter, no output schema, no annotations), the description fully covers the necessary context: what it does, what input it expects with examples, and what the output contains. No important aspect is missing for a glossary lookup tool.

    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 schema already describes the parameter ('The term name or slug to define') with 100% coverage. The description adds concrete examples ('account suspension' or 'account-suspension'), which aids the agent in formatting input correctly. This improves upon the baseline of 3.

    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 action ('Define a term'), specifies the domain ('AuditSocials Compliance Glossary'), and provides examples of input format. It is distinct from sibling tools glossary_search (which likely searches) and glossary_list_categories (which lists categories), making the purpose unambiguous.

    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 explains what the tool returns (definition, category, platforms, related terms), which helps an agent decide when to use it. However, it does not explicitly contrast with sibling tools or provide when-not-to-use guidance, leaving the decision partially implicit.

    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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