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singhpratech

crimson-crab-mcp-template

by singhpratech
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Rust
Remote

Server Quality Checklist

83%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.2.0

  • Disambiguation5/5

    Each tool has a distinct purpose: ask_claude handles queries, count_tokens deals with token counting, and list_models retrieves available models. There is no functional overlap.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern in snake_case (ask_claude, count_tokens, list_models), making them predictable and easy to use.

    Tool Count5/5

    With 3 tools, the server is lean and focused on core Claude API interactions. This is appropriate for a minimal template without unnecessary bloat.

    Completeness4/5

    The tools cover essential operations: asking questions, counting tokens, and listing models. Minor gaps exist, such as missing streaming or model details beyond listing, but the set is sufficient for basic use.

  • Average 3.5/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 11 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under Apache 2.0.

  • 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.

  • This repository includes a glama.json configuration file.

  • This server has been verified by its author.

  • Add related servers to improve discoverability.

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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?

    No annotations are given, so the description must fully disclose behavior. It only states the basic action (ask and answer) without mentioning safety, idempotency, statefulness, rate limits, or any other behavioral traits.

    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, clear sentence with no unnecessary words. It is optimally concise for the information provided.

    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?

    For a simple Q&A tool with no output schema, the description covers the basic purpose but lacks context on usage scope, response format, or limitations. It is minimally adequate.

    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 clear descriptions for both parameters. The tool description adds no extra meaning beyond the schema, so baseline score of 3 is appropriate.

    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 asks Claude a question and returns an answer, with a specific verb and resource. However, there are no sibling tools to distinguish from, so it loses the chance for a higher score on differentiation.

    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, nor are there any conditions or exclusions mentioned. The description is purely functional without context.

    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 carries the full burden. However, it does not disclose any behavioral traits such as authentication requirements, rate limits, or whether the list is sorted. For a simple read-only list, this is a minor gap.

    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 consists of one concise sentence that covers the core functionality without any extraneous words. It is efficiently front-loaded with the essential information.

    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?

    Given the tool's simplicity (one optional parameter, no output schema), the description is sufficient for understanding the primary purpose. However, it does not specify what the return value contains (e.g., model IDs, names) or any pagination behavior, which could leave some uncertainty for the agent.

    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%: the single parameter 'limit' has a clear description in the schema. The description does not add additional meaning beyond what the schema already provides, so baseline 3 applies.

    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 explicitly states the action ('list'), the resource ('Claude models'), and the scope ('available to the configured Anthropic API key'). It clearly distinguishes from sibling tools 'ask_claude' and 'count_tokens' by focusing on model listing.

    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 when the agent needs to query available models, but does not provide explicit guidance on when to use vs. alternatives, nor does it mention any prerequisites or exclusions. Sibling tools have different purposes, so ambiguity is low.

    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?

    The description accurately conveys that the tool counts tokens without sending the prompt, which is appropriate for a low-risk tool. However, it does not mention that token counts are model-specific or other potential nuances.

    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 consists of a single, front-loaded sentence with no superfluous words. It efficiently conveys the tool's purpose.

    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 simple estimation tool with no output schema, the description adequately covers the core functionality. It could mention the return format, but that is relatively minor.

    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?

    The input schema already provides descriptions for all three parameters (100% coverage). The tool description adds no additional meaning beyond what the schema provides.

    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 that the tool counts input tokens for a given Claude model without sending the prompt. It uses specific verbs and resources, and it distinguishes from sibling tools like 'ask_claude' and 'list_models'.

    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 that the tool is for estimating token usage before using 'ask_claude', but it does not explicitly state when to use it versus alternatives or provide any exclusions or prerequisites.

    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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