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MSAdministrator

Enrichment MCP Server

by MSAdministrator
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Python
Remote

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 single tool 'lookup-observable' has a clear, distinct purpose of routing observables to appropriate handlers, leaving no room for confusion or misselection.

    Naming Consistency5/5

    The naming is trivially consistent as there is only one tool. The tool name 'lookup-observable' follows a clear verb_noun pattern, and with no other tools to compare, there is no inconsistency in naming conventions.

    Tool Count2/5

    A single tool is too few for a server named 'Enrichment MCP Server', which suggests a purpose of enriching various observables. This minimal toolset feels thin and inadequate for the implied scope, as it relies on internal routing rather than exposing a comprehensive set of enrichment operations directly.

    Completeness1/5

    The tool surface is severely incomplete for an enrichment server. There are obvious gaps, such as no direct tools for specific observable types (e.g., IP addresses, domains, files) or enrichment actions (e.g., threat intelligence lookup, geolocation). The single generic tool creates a dead end for agents, lacking the necessary coverage for the domain.

  • Average 1.6/5 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

  • Behavior1/5

    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 fails to describe any traits—such as whether it's read-only, destructive, requires authentication, or has rate limits—and does not explain what 'passes it' means in terms of output or side effects.

    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 description is a single sentence, which is appropriately concise, but it is not front-loaded with critical information. It wastes space on vague phrasing like 'generic tool' without adding value, though it avoids excessive verbosity.

    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 tool's complexity (with an output schema but no annotations and 0% schema coverage), the description is incomplete. It does not clarify the tool's purpose, parameters, or behavior, failing to compensate for the lack of structured data, though the output schema might help mitigate some gaps.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters1/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 0%, and the description does not add any meaning beyond the schema. It does not explain what 'value' represents (e.g., what an 'observable' is), its format, or constraints, leaving the single parameter undocumented and unclear.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose2/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description states 'takes any observable and passes it the correct tool,' which is tautological—it restates the tool's name 'lookup-observable' without specifying what an 'observable' is or what 'passes it' entails. It lacks a clear verb+resource combination, making the purpose vague and minimally informative.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines1/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    There is no guidance on when to use this tool, such as context, prerequisites, or alternatives. The description is generic and does not provide any usage instructions, leaving the agent with no direction on its application.

    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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