Skip to main content
Glama
EgorKluch

LAML MCP Server

by EgorKluch

Server Quality Checklist

50%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly distinct as it is the sole operation available.

    Naming Consistency5/5

    The single tool name 'loadLaml' uses a consistent verb_noun pattern (load + Laml). Since there is only one tool, naming consistency is inherently perfect with no deviations to assess.

    Tool Count2/5

    A single tool is too few for a server's purpose, as it severely limits functionality and suggests an incomplete or trivial implementation. This is a borderline case leaning towards inadequacy.

    Completeness1/5

    The server is severely incomplete for handling LAML documents; it only loads documents without any operations for creating, updating, deleting, querying, or validating them, leaving significant gaps in the domain coverage.

  • Average 2.9/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 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. It mentions loading from a file path but fails to describe critical behaviors such as error handling (e.g., what happens if the file doesn't exist or is malformed), performance characteristics, or any side effects. This leaves significant gaps in understanding how the tool behaves in practice.

    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, direct sentence that efficiently conveys the core functionality without unnecessary words. It is front-loaded with the essential action and resource, making it easy to parse quickly. There is no wasted verbiage, achieving optimal conciseness.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the absence of annotations and output schema, the description is incomplete for a tool with two required parameters. It lacks details on return values, error conditions, or operational constraints, leaving the agent with insufficient information to use the tool effectively in complex scenarios. The high schema coverage doesn't compensate for these missing behavioral aspects.

    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%, with both parameters ('project' and 'path') clearly documented in the schema as absolute paths. The description adds no additional semantic context beyond implying file-based loading, which is already inferred from the schema. This meets the baseline for high schema coverage but doesn't enhance parameter understanding.

    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 action ('Load') and resource ('LAML document from file path'), making the purpose immediately understandable. It specifies the source as a file path, which distinguishes it from potential alternatives like loading from a URL or database. However, without sibling tools for comparison, the differentiation aspect is inherently limited.

    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, or constraints. It merely states what the tool does without context for its application. With no sibling tools mentioned, there's no explicit comparison, but the lack of any usage context leaves the agent without operational guidance.

    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

mcp-laml MCP server

Copy to your README.md:

Score Badge

mcp-laml 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/EgorKluch/mcp-laml'

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