Skip to main content
Glama
owayo

MCP Source Relation Server

by owayo
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

58%
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 has a clearly defined purpose of analyzing dependencies between source files, making it impossible for an agent to misselect between non-existent alternatives.

    Naming Consistency5/5

    A single tool inherently exhibits perfect naming consistency, as there are no other tools to compare against. The tool name 'get_source_relation' follows a clear verb_noun pattern, which would be consistent if more tools existed.

    Tool Count2/5

    A single tool is generally too few for most server purposes, as it limits functionality and suggests an incomplete or overly narrow scope. While it might be appropriate for a highly specialized task, it feels thin and insufficient for comprehensive source dependency analysis, which typically involves multiple operations like listing, updating, or visualizing relations.

    Completeness2/5

    The tool set is severely incomplete for the inferred domain of source dependency analysis. With only a 'get' operation, there are obvious gaps such as creating, updating, deleting, or listing source relations, and no support for lifecycle management or advanced queries. This will likely cause agent failures when more complex operations are needed.

  • Average 2.1/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 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

  • 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

  • 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. The description only states what the tool does at a high level ('analyze dependencies'), but doesn't disclose behavioral traits like whether this is a read-only operation, what format the analysis returns, whether it has side effects, performance characteristics, 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?

    The description is extremely concise at just 5 words with no wasted language. It's front-loaded with the core purpose and uses efficient phrasing. Every word earns its place in conveying the basic function.

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

    Completeness1/5

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

    Given the tool has no annotations, no output schema, and 0% schema description coverage, the description is completely inadequate. For a tool that performs analysis (potentially complex), the description provides minimal context about what analysis means, what results to expect, or how to interpret them.

    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?

    The description provides no information about parameters. With 0% schema description coverage and a single required parameter 'path', the description doesn't compensate at all - it doesn't explain what the path parameter represents, what format it expects, or how it relates to the dependency analysis.

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

    Purpose3/5

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

    The description 'Analyze dependencies between source files' states a general purpose (analyzing dependencies) and resource (source files), but lacks specificity about what kind of analysis is performed or what 'dependencies' means in this context. It doesn't distinguish from siblings, but there are no sibling tools to differentiate from.

    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?

    No guidance is provided on when to use this tool versus alternatives, prerequisites, or constraints. The description implies it's for dependency analysis, but doesn't specify scenarios where this is appropriate or what problems it solves.

    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-source-relation MCP server

Copy to your README.md:

Score Badge

mcp-source-relation 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/owayo/mcp-source-relation'

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