Skip to main content
Glama
Hoseong21

github-portfolio-mcp

by Hoseong21
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
Python
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation4/5

    Each tool has a distinct purpose, but search_repos and list_repos both return repository names and descriptions, with the only difference being keyword filtering. This overlap is manageable but could cause minor selection confusion.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern with snake_case: search_repos, get_readme, list_repos. The naming convention is uniform and predictable.

    Tool Count5/5

    With only 3 tools, the set is tightly scoped to a portfolio viewing purpose. Each tool is essential and there is no clutter or redundancy.

    Completeness5/5

    For a read-only portfolio server, the tools cover the core workflows: listing all repositories, searching by keyword, and retrieving a README. No critical operations are missing given the stated purpose.

  • Average 4/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 7 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

  • Behavior3/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    No annotations are provided, so the description carries the behavioral burden. It adds the useful constraint that repo_name must be exact and provides an example, but it does not disclose error behavior, access requirements, or any edge cases. This is minimal but acceptable for a simple read operation.

    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 very short, front-loaded with the core purpose, and includes a compact parameter explanation. Every sentence earns its place with no filler.

    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 single-parameter tool with an output schema, the description is adequate but not fully complete. It lacks sibling-routing guidance and behavioral caveats, though these are less critical given the tool's simplicity.

    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?

    Schema description coverage is 0%, so the description must compensate. It explains that repo_name means the exact repository name and gives a concrete example, adding value beyond the schema's plain string type.

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

    Purpose5/5

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

    The description states a specific verb and resource: fetching the README content of a specific repository. This clearly distinguishes the tool from the sibling search_repos and list_repos tools, which operate on repo discovery/listing rather than README retrieval.

    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 gives no guidance on when to use this tool instead of search_repos or list_repos, and does not mention any prerequisites or exclusions. An agent must infer that this is for retrieving READMEs from already-known repositories.

    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 are present, so the description carries the full burden. It does disclose the operational scope (only public repos), the resource owner (the GitHub account), and the returned content (names and descriptions). However, it does not mention authentication requirements, pagination, rate limits, or ordering, which are relevant for a listing endpoint.

    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, focused sentence with no filler or redundant information. It front-loads the action and resource, making it easy for an agent to understand the tool quickly.

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

    Completeness4/5

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

    For a parameterless list tool with an output schema and simple sibling context, the description is largely complete. It states the scope, subject, and included fields. It could slightly improve by noting that this is a read-only operation and whether the public-repo scope is the only intended use case.

    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 tool has zero parameters and the schema description coverage is 100%, so there are no parameters to document. The description adds meaningful context about what the result will include, which is sufficient for a parameterless tool.

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

    Purpose5/5

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

    The description states a specific action ('가져옵니다' fetches) and a specific resource: all public repositories in the user's GitHub account, with names and descriptions. This clearly differentiates the tool from siblings like search_repos, which searches across repositories rather than listing the account's own public repos.

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

    Usage Guidelines3/5

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

    The description implies the tool is for listing the authenticated user's public repositories, but it does not explicitly state when to prefer this over search_repos or get_readme. There is no exclusionary guidance or mention of alternatives, so usage context is only implied rather than directed.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior4/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    The description discloses the behavior (searching and returning matching repos) and indicates a read-only action. However, it does not mention potential side effects, return format, or error cases, though these are not critical for a search tool.

    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 concise and focused, containing only the essential information: the tool's purpose and the parameter definition. No unnecessary words or redundant details.

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

    Completeness5/5

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

    For a simple search tool, the description provides sufficient context: it specifies what is searched (repo names and descriptions) and what the keyword is. No additional context needed for correct usage.

    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 description explicitly defines the 'keyword' parameter as the search term, which adds meaning beyond the bare schema type. Since the parameter coverage is 100% and the description enriches it, a score above baseline is warranted.

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

    Purpose5/5

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

    The description clearly states the tool's function: searching repository names and descriptions by keyword. It is specific and distinct from sibling tools (list_repos lists all repos, get_readme fetches a specific repo's readme).

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

    Usage Guidelines3/5

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

    The description does not explicitly mention when to use this tool versus alternatives like list_repos or get_readme. The intent is implied but not stated, leaving some ambiguity for the agent.

    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

github-portfolio-mcp MCP server – quality and maintenance score on Glama

Copy to your README.md:

Score Badge

github-portfolio-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/Hoseong21/github-portfolio-mcp'

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