Skip to main content
Glama
edushinka

betbetter-mcp

by edushinka
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.1

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: listing leagues, getting picks for a league, and searching fixtures. No overlap in functionality.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern (get_model_picks, find_fixture, list_leagues), using underscores and clear verbs.

    Tool Count3/5

    With only 3 tools, the server is minimal but covers the core querying needs for model predictions. Could benefit from additional tools for direct fixture access or historical data.

    Completeness2/5

    Lacks a direct way to retrieve picks for a specific fixture by ID or to get predictions without searching by name. The search tool is a workaround but not a direct lookup.

  • Average 3.9/5 across 3 of 3 tools scored. Lowest: 3.3/5.

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

    • No community issues in the last 6 months
    • 3 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?

    No annotations provided, so the description carries full burden. It indicates a read-only search operation (no destructive hints) but does not explicitly state safety, authentication needs, or rate limits. Adequate but could be more transparent.

    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?

    Single sentence, no redundancy. Essential information is front-loaded. Every word contributes to understanding the tool's function.

    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?

    No output schema, so description should explain return values; it says 'rated selections' but lacks specifics on structure. Given only 2 parameters and a straightforward search, it is moderately complete but could benefit from example or field details.

    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%, so baseline is 3. The description adds some meaning by specifying 'team or player name' for query, but this largely overlaps with the schema's description. No additional guidance on limit parameter.

    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 tool searches fixtures by team/player name and returns rated selections. It implies cross-league scope, distinguishing it from list_leagues (listing leagues) and get_model_picks (likely picks without search), but does not explicitly differentiate from siblings.

    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 on when to use this tool vs alternatives (get_model_picks, list_leagues). The description only explains what it does, not when it's preferable or when not to use it.

    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?

    With no annotations, the description carries full burden. It transparently states it is model output only and what is excluded. It could mention additional context like data freshness or auth requirements, but it sufficiently covers the key behavioral trait of not including betting prices.

    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?

    Two concise sentences, no extra words. First sentence states what it returns, second clarifies a critical limitation. Every sentence is essential and front-loaded with the most important information.

    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?

    Despite no output schema, the description mentions the return fields (probability, odds, confidence). It also sets expectations by clarifying it does not contain bookmaker prices. Could be more complete about the ordering or selection logic, but overall adequate for a simple data retrieval tool.

    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%, so the schema documents parameters well. The description adds value by summarizing the overall purpose (model picks) but does not elaborate on parameter details beyond the schema. It meets the baseline expectation for high-coverage schemas.

    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?

    Description clearly states the tool returns the model's rated selections with specific attributes (win probability, fair decimal odds, confidence word). It distinguishes from siblings like find_fixture and list_leagues by focusing on model picks, not fixtures or league lists.

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

    Usage Guidelines4/5

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

    Explicitly notes the tool contains no bookmaker prices and cannot tell where to bet, advising when not to use it. However, it does not provide guidance on when to use this tool versus siblings (e.g., for model ratings vs. fixture lookup).

    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 provided, so the description must disclose behavioral traits. It accurately describes the output but does not explicitly state it is a read-only operation or mention any 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.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single sentence of 75 characters with no unnecessary words. It is perfectly concise and front-loaded.

    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 that there are no parameters or output schema, the description covers the essential purpose and usage. It could optionally mention the output format, but it is complete enough.

    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 input schema has no properties (0 parameters), and schema coverage is 100%. The description adds meaning by specifying what is listed, which is not captured in the schema.

    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 lists every league slug for use with other tools, with a specific verb ('list') and resource ('league slugs'). It distinguishes from siblings by noting its output is intended for them.

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

    Usage Guidelines4/5

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

    The phrase 'for use with the other tools' implies it is a prerequisite for siblings, providing clear context. However, it does not explicitly state when to use it or provide alternatives.

    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

betbetter-mcp MCP server

Copy to your README.md:

Score Badge

betbetter-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/edushinka/betbetter-mcp'

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