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Opafex

Intelagent MCP Template

by Opafex
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TypeScript

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: reverse_string manipulates string order, word_count analyzes string content, and server_info provides metadata. There is no overlap in functionality, making tool selection unambiguous.

    Naming Consistency5/5

    All tool names follow a consistent snake_case pattern with clear verb_noun structure (reverse_string, server_info, word_count). The naming is predictable and readable throughout the set.

    Tool Count3/5

    With only 3 tools, the set feels thin for a general-purpose 'Intelagent MCP Template' server, suggesting limited scope or utility. However, the count is not extreme and could be appropriate for a minimal template.

    Completeness3/5

    For a template server, the tools cover basic string operations and introspection, but there are notable gaps in common utility functions (e.g., text formatting, mathematical operations). The surface is functional but incomplete for broader agent tasks.

  • Average 3.9/5 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 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

  • Behavior3/5

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

    No annotations provided, so description carries full burden. It discloses core functionality (counting words/characters) and the unique word option, but omits details about word tokenization rules, case sensitivity, or idempotency/safety characteristics.

    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?

    Extremely efficient two-sentence structure. First sentence establishes primary function; second sentence covers optional behavior. No redundant or filler content—every word 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 the low complexity (2 primitive parameters, no nested objects) and absence of output schema, the description adequately covers intent but would benefit from specifying the return structure (e.g., object with word_count and char_count fields).

    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 'text' and 'unique' parameters. Description adds minimal semantic value beyond schema, only mapping 'optionally count unique words' to the boolean flag, which aligns with baseline expectations for fully documented schemas.

    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 the specific action (count) and resources (words, characters) with scope (unique words optional). Distinct from sibling 'reverse_string' (transformation) and 'server_info' (metadata).

    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?

    No explicit when-to-use or when-not-to-use guidance provided. However, the tool's purpose is self-evident from the name and description, providing implied usage context without explicit alternative recommendations.

    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 provided, the description carries the full burden of behavioral disclosure. It compensates for the missing output schema by documenting the return structure (original text, reversed text, character count), but omits other behavioral traits like idempotency, safety guarantees, or error conditions.

    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 sentences with zero waste: the first defines the action, the second discloses return values. Information is front-loaded and appropriate for the tool's simplicity. Every sentence earns its place.

    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?

    For a single-parameter utility without annotations or output schema, the description is complete. It adequately compensates for the missing output schema by detailing the three return fields, providing sufficient context for an agent to invoke the tool correctly.

    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 has 100% description coverage ('The text to reverse'), establishing a baseline of 3. The description adds no additional parameter context (examples, format constraints, validation rules) beyond what the schema already 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 states a specific verb ('Reverse') and resource ('string'), clearly distinguishing it from sibling tools: server_info (metadata) and word_count (counting). The first sentence precisely defines the tool's function.

    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 through the specific purpose statement ('Reverse a string'), but provides no explicit when-to-use guidance, prerequisites, or comparison to alternatives like word_count. The agent must infer appropriateness from the function name.

    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 provided, the description carries the full burden. It discloses what data is returned but omits safety characteristics (read-only vs destructive), caching behavior, or performance implications that would help an agent understand operational constraints.

    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 well-structured sentences with zero waste. First sentence front-loads the action and return values; second sentence provides usage context. Every word 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 the tool's simplicity (no parameters) and lack of output schema, the description adequately compensates by enumerating the returned metadata fields. Could improve by noting whether data is cached or real-time, but sufficient for agent selection.

    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?

    Input schema contains zero parameters, establishing a baseline of 4 per evaluation rules. No parameter explanation is required or provided.

    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 specific verb 'Returns' with concrete resource details (server metadata: name, version, registered tools, resources). It clearly distinguishes this introspection tool from string-manipulation siblings like reverse_string and word_count.

    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?

    States it's 'Useful for introspection and health checks,' providing implied context for usage. However, it lacks explicit when-to-use comparisons against alternatives or conditions where this tool is preferred.

    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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