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Server Quality Checklist

67%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool serves a distinct, non-overlapping purpose: one compares ask prices, one ranks multiple candidates, and one evaluates a single name. No ambiguity.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern in snake_case, using verbs like check, compare, and score.

    Tool Count5/5

    Three tools is appropriate for a focused server that handles Telegram username valuation and comparison. Not too few or too many.

    Completeness4/5

    Covers core valuation and comparison operations. Lacks direct buy/mint actions, but these are not valuation tasks. Minor gap in lifecycle coverage.

  • Average 3.8/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 8 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 MIT License.

  • This repository includes a README.md file.

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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

  • Behavior2/5

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

    No annotations provided; description only states it scores and ranks but does not describe scoring methodology, return values, or whether it is a read/mutation operation. Limited behavioral insight.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    Two concise sentences, front-loaded with key purpose. Efficient but could include more structured bullet points 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?

    Adequate for basic usage but lacks behavioral details and parameter semantics. No output schema makes return type unclear. With 2 params and siblings, minimal but acceptable completeness.

    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?

    Schema coverage is 50% with 'names' described as 'Candidate usernames' in schema. Description adds no new parameter details; 'lang' and scoring logic remain unexplained.

    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 it scores and ranks multiple username candidates (max 20), distinguishing it from sibling 'score_name' which likely handles single names. Specific verb 'score' and resource 'username candidates' are present.

    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 says 'Use when choosing between name options for a brand, bot, channel or investment.' Provides clear context for use but does not mention when not to use or alternatives.

    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?

    No annotations are provided, so the description carries the full burden. It explains the output (fair/ask ratio) and provides a threshold interpretation (>=1.2 means underpriced). However, it does not disclose error cases (e.g., nonexistent name), side effects, authentication needs, or rate limits. The basic behavior is clear but lacks depth.

    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 very concise: two short sentences that front-load the purpose and immediately provide actionable output interpretation. Every word contributes meaning.

    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 tool's straightforward nature (two parameters, no nested objects, no output schema), the description adequately explains the return value and its interpretation. It does not explain what 'class valuation' is, but that is a domain concept the agent might infer. The context is sufficient for a simple check tool.

    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 descriptions for both parameters. The description adds context about 'Fragment asking price' and 'class valuation', but does not clarify what 'class valuation' means or how it is computed. Since schema already covers parameter details, the description provides marginal added value.

    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: comparing a Fragment asking price against the name's class valuation and returning a ratio. It uses specific verbs ('compare', 'returns') and identifies the resource (Fragment ask price). It distinguishes from siblings (compare_names, score_name) by focusing on a specific pre-purchase check.

    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 indicates when to use the tool ('Pre-purchase check'), but does not explicitly state when not to use it or provide alternatives. It could be improved by mentioning that compare_names or score_name might be used for other purposes, but the context is clear enough.

    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?

    No annotations are provided, so the description carries the full burden. It discloses that scoring is validated against real Fragment sales and mentions the outputs, but it does not describe any behavioral traits such as side effects, authentication needs, or rate limits. For a read-only scoring tool, 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?

    The description is a single, well-structured sentence that front-loads the primary purpose and key outputs. Every word serves a purpose, with no redundancy or wasted space.

    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 output schema, the description adequately explains the return values and their significance (validated against real sales). It covers the main inputs and usage context. Minor omission: does not state whether the tool is read-only or requires authentication, but for a scoring tool this is acceptable.

    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 the baseline is 3. The description doesn't add significant new information about parameters beyond the schema; it explains the output context (validation vs Fragment sales, GRAM price) but doesn't clarify parameter usage beyond what is already in 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 uses the specific verb 'Score' and identifies the resource as 'Telegram username'. It lists the outputs (namability 0-100, quality band, fair price, positioning thesis) and distinguishes from siblings like check_fragment_ask and compare_names via usage guidance.

    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 states when to use the tool: 'when the user asks whether a username is good, what it is worth, or before buying/minting it.' While it does not explicitly mention when not to use or list alternatives, the sibling tools provide implied alternatives and the context is clear.

    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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