Skip to main content
Glama
chaitanyaatlan

Fifth Elephant MCP Server

by chaitanyaatlan
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

50%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: one performs a mathematical addition operation, while the other returns a greeting message. There is no overlap or ambiguity between them.

    Naming Consistency2/5

    The naming is inconsistent: 'add' uses a simple verb, while 'hello_world' uses a snake_case compound phrase. There is no predictable pattern across the tool set.

    Tool Count2/5

    With only two tools, the server feels thin and under-scoped for a general-purpose MCP server, lacking coverage for a coherent domain or meaningful workflows.

    Completeness1/5

    The tool surface is severely incomplete; there is no discernible domain (e.g., math, utilities), and the two tools do not support any meaningful operations or lifecycle, leaving obvious gaps for agent functionality.

  • Average 3.3/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 0 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

  • 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 of behavioral disclosure. While 'Returns' implies a read-only operation, the description fails to specify side effects, idempotency, error conditions, or the format/structure of the returned greeting.

    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?

    At four words, the description is efficiently compact with no extraneous information. It front-loads the action and object, making it immediately scannable.

    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 trivial complexity (zero parameters, no nested objects) and lack of output schema, the minimal description is adequate but incomplete. It does not specify the return value's data type, format, or content beyond the vague 'friendly greeting', leaving the agent uncertain about what exactly will be returned.

    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 input schema contains zero parameters. Per the baseline rule for zero-parameter tools, this dimension scores a 4. The description correctly implies no input is required by focusing solely on the return value.

    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 uses a specific verb (Returns) and resource (greeting) that clearly communicates the tool's function. While it implicitly distinguishes from the sibling 'add' tool (greeting vs. arithmetic), it does not explicitly state this differentiation or clarify what makes the greeting 'friendly'.

    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?

    The description provides no guidance on when to use this tool versus alternatives, prerequisites for invocation, or conditions where it should be avoided. It merely states what the tool does without contextual usage advice.

    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 provided, so description carries full burden. States the mathematical operation but omits details like return value format, overflow behavior, or whether the operation is atomic/reversible.

    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?

    Single sentence with zero waste. Front-loaded and appropriately sized for the tool's trivial complexity.

    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 the low complexity (2 integer params, no nested objects), but lacks return value description and error handling details given the absence of output schema or annotations.

    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 0% (titles 'A' and 'B' not sufficient). Description mentions 'two numbers' implying the parameter count and types generally, but does not map them to parameters 'a' and 'b' or explain their semantic roles (e.g., addends).

    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?

    Clear verb ('adds') and resource ('two numbers'), but does not explicitly differentiate from sibling 'hello_world' tool (though the distinction is obvious).

    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?

    Provides no guidance on when to use this tool versus alternatives, nor any prerequisites or contextual conditions for invocation.

    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

fifth-elephant-mcp-workshop MCP server

Copy to your README.md:

Score Badge

fifth-elephant-mcp-workshop 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/chaitanyaatlan/fifth-elephant-mcp-workshop'

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