Skip to main content
Glama
brandikun

steam-storefront-mcp

by brandikun
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.2

  • Disambiguation5/5

    Each tool has a distinct purpose: search for games, get metadata, and get reviews. There is no overlap between searching, fetching details, and fetching reviews, so an agent will not confuse them.

    Naming Consistency5/5

    All tool names follow a clear verb_noun pattern: search_store, get_store_details, get_app_reviews. The consistent use of imperative verbs and specific resource nouns makes the set predictable.

    Tool Count5/5

    Three tools is an appropriate, focused scope for a Steam storefront server. Each tool provides a necessary capability without unnecessary bloat.

    Completeness4/5

    The core workflow of searching for a game and viewing its details and reviews is fully covered. Minor gaps exist such as listing trending or new releases, but for the stated purpose of storefront metadata and reviews, the surface is complete enough.

  • Average 3.8/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 5 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

  • Behavior3/5

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

    With no annotations, the description carries the full burden of behavioral disclosure. It explains what the tool returns (rating, total reviews, positive ratio) and implicitly indicates a read operation. However, it does not mention any potential side effects, data freshness, or limitations, which is a minor gap given the simple read-only nature.

    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, front-loaded sentence that concisely states the verb, resource, and specific output fields. Every word adds value, with no redundancy or filler.

    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 tool with one parameter and no output schema, the description covers the essential context by naming the return fields and the domain (Steam reviews). It is somewhat minimal but sufficient for a simple retrieval tool, though it could benefit from a brief note about how 'summaries' are calculated or aggregated.

    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 coverage is 100%, and the parameter 'appId' is already well described as 'The numeric Steam AppID'. The description adds only general context about Steam reviews, but no extra parameter semantics beyond what the schema provides. Baseline 3 is appropriate.

    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 what the tool does: 'Get Steam player review summaries' and lists specific output elements (review score rating, total reviews, positive ratio). This distinguishes it from sibling tools like search_store and get_store_details, which focus on different data.

    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 usage when review summaries are needed, but it does not explicitly mention when to use this tool vs alternatives or any exclusions. No alternative tools are referenced in the description, so guidance is only implied from the clear purpose.

    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?

    With no annotations, the description must carry behavioral disclosure. It states the tool retrieves read-only storefront metadata and lists output fields, which is transparent about the expected result. However, it does not mention any side effects, authentication requirements, or failure cases, leaving some behavioral gaps.

    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?

    One sentence, front-loaded with the action and resource, with parenthetical examples of fields. No wasted words.

    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 single-parameter getter, the description adequately conveys the tool's scope and return content by listing key fields. It lacks an explicit return type but is otherwise sufficient.

    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?

    The input schema fully documents appId with an example, and schema coverage is 100%. The description adds no parameter-specific semantics beyond confirming it identifies a game, so the baseline of 3 applies.

    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 uses the specific verb 'Get' with a resource 'detailed Steam storefront metadata' and enumerates expected fields (genres, categories, description, developers, metacritic, release date), clearly distinguishing it from siblings like search_store (discovery) and get_app_reviews (reviews).

    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?

    No explicit guidance about when to use this tool versus siblings is provided. The phrasing implies it is for fetching known app details, but there is no direct mention of alternatives or exclusions, so usage guidance is only implied.

    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?

    With no annotations, the description must carry the full burden. It states the core operation (search) and the output (AppIDs), but it does not disclose details such as response format, pagination, rate limits, or side effects. It is not contradictory, but lacks comprehensive behavioral context.

    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, front-loaded sentence with no filler. It efficiently conveys the action, resource, and purpose.

    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?

    Given the tool's simplicity (one parameter, no output schema), the description states the purpose and key output (AppIDs) sufficiently for basic use. It does not specify the exact response structure, but for a search tool with low complexity, it is mostly complete.

    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?

    The schema provides 100% coverage with a clear description of the 'term' parameter, including examples. The tool description adds no additional semantic information beyond the schema, so the baseline of 3 is appropriate.

    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 uses the specific verb 'Search' and identifies the resource 'Steam Storefront', clearly stating the outcome 'to get their Steam AppIDs'. This distinguishes it from sibling tools like get_store_details and get_app_reviews, which serve different functions.

    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 finding AppIDs by search query, but it does not explicitly state when to use it versus alternatives or provide exclusions. There is no direct guidance on tool selection beyond the inferred purpose.

    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

steam-storefront-mcp MCP server

Copy to your README.md:

Score Badge

steam-storefront-mcp 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/brandikun/steam-storefront-mcp'

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