Skip to main content
Glama
scalably-io

example-mcp

by scalably-io
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
Python
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of confusion between tools. The single tool clearly performs an echo operation with no overlapping purposes.

    Naming Consistency3/5

    The sole tool name 'example_echo' does not follow a clear verb_noun pattern ('example' is not a verb), but there is no inconsistency to compare against. The name is readable but not representative of a standard naming convention.

    Tool Count2/5

    With only one tool, the server is far below the typical scope of 3-15 tools. The tool appears to be a placeholder, and the count feels excessively thin for a functional MCP server.

    Completeness2/5

    The description explicitly states 'Replace with real tools,' indicating this is a stub rather than a complete domain surface. There is no coherent set of operations to cover a real workflow, leaving significant gaps for any practical use.

  • Average 3.1/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 10 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 passing

  • 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?

    The readOnlyHint annotation already indicates this is a safe, non-destructive operation. The description adds that the tool simply returns the input unchanged, which is consistent with the annotation. However, 'Replace with real tools' creates ambiguity about whether this tool is a functional stub, and no additional behavioral context such as output formatting is provided.

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

    Conciseness3/5

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

    The first sentence is concise and front-loaded: 'Return the text you pass in.' The second sentence, 'Replace with real tools,' is a developer-facing placeholder instruction that does not help an agent invoke the tool correctly and should be removed or replaced.

    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 trivial echo operation with a read-only annotation and a single obvious parameter, the essential information is present. However, the placeholder comment undermines certainty about whether the tool is production-ready, and there is no explanation of typical use cases or expected behavior beyond the basic echo.

    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 0%, so the description must compensate for the undocumented 'text' parameter. It does map the parameter to behavior by saying 'the text you pass in' is returned, but it adds no constraints, examples, or formatting details. For a single trivial string parameter this is minimally adequate, though not comprehensive.

    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 states a clear, specific action: 'Return the text you pass in.' This identifies the tool as an echo/reflection tool and distinguishes it from generic processing tools. The appended 'Replace with real tools' is a placeholder note that slightly weakens the clarity, so it is not a perfect 5.

    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 about when to use this tool or when an alternative would be preferable. There are no sibling tools listed, and the placeholder phrase 'Replace with real tools' does not clarify usage context or intended scenarios.

    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

example-mcp MCP server – quality and maintenance score on Glama

Copy to your README.md:

Score Badge

example-mcp MCP server – quality and maintenance score on Glama

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/scalably-io/mcp-server-template-python'

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