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0xAndoroid

poker-bankroll-tracker-mcp

by 0xAndoroid
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v1.2.0

  • Disambiguation5/5

    get_sessions retrieves individual session records with filters, while get_stats computes aggregate metrics. Their purposes are clearly distinct and unlikely to be confused.

    Naming Consistency5/5

    Both tools follow a consistent get_<plural> pattern, which is predictable and easy to understand. There's no mixing of styles or vague verbs.

    Tool Count3/5

    With only 2 tools, the server feels thin for a tracker. While the two tools are well-focused, the small number suggests the surface may be underdeveloped for broader use.

    Completeness2/5

    The tool surface only supports reading and analysis of sessions. There are no tools to create, update, or delete sessions, which are core to managing a bankroll. This is a significant gap that prevents agents from maintaining data.

  • Average 4.1/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 2 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.

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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?

    Annotations already declare readOnlyHint=true, so the safety profile is covered. The description adds no additional behavioral context beyond the aggregation logic described in the output list (e.g., grouping by location/stakes/month), which is more about output structure than side effects. No contradiction, but no extra transparency beyond annotations.

    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?

    This is a single sentence that front-loads the verb 'Compute' and immediately specifies the resource and outputs. The list of breakdowns is concise and useful, with 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?

    Given the tool has no output schema and only a readOnly annotation, the description adequately explains what the tool returns (total profit, win rate, etc.) and the grouping dimensions. However, it omits default behavior when optional parameters are omitted (e.g., whether stats cover all time or all sessions), leaving a minor gap.

    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 provides 100% description coverage for all five parameters, including date range, type, staking, and currency. The description does not add any detail about parameter formats or defaults beyond what the schema already gives, so it adds no incremental value here (baseline 3).

    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 opens with 'Compute aggregate statistics from poker sessions,' a specific verb+resource that clearly distinguishes this from the sibling get_sessions tool, which presumably returns raw session data. It enumerates specific outputs (total profit, win rate, etc.), making the purpose unambiguous.

    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 description implies that this tool is for aggregate/statistical queries, contrasting with get_sessions for raw data, but it does not explicitly state when to use one versus the other or provide exclusions. The context is clear, but there is no direct mention of alternatives.

    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?

    Beyond the readOnlyHint annotation, the description discloses that results include calculated profit/loss and warns about significant token consumption for broad date ranges. This adds useful behavioral context about return values and cost implications.

    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 two sentences and front-loads the core purpose, then immediately provides a critical usage warning. Every sentence earns its place with no redundancy.

    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 read-only tool with 5 optional parameters and full schema coverage, the description adequately explains the purpose, mentions the return includes profit/loss (since no output schema exists), and adds the token warning. It omits potential details like response structure or pagination, but the essential context is covered.

    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 already covers all 5 parameters with descriptions (100% coverage). The tool description only generically mentions 'optional filters' and adds a date-range warning, without adding new parameter-level meaning beyond what the schema provides.

    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 fetches poker sessions with optional filters and notes it returns calculated profit/loss. This distinguishes it from the sibling get_stats by implying raw session data rather than aggregated statistics.

    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?

    It provides clear context on when to use the tool (fetching sessions) and includes a practical warning about broad date ranges consuming tokens, advising narrow ranges. However, it doesn't explicitly compare to the sibling tool get_stats, so no alternatives or exclusions are mentioned.

    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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