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Jwrede

tokentoll

by Jwrede
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

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

  • Latest release: v0.8.3

  • Disambiguation5/5

    The two tools, diff and scan, have clearly distinct purposes: scan finds LLM call sites and estimates costs, while diff compares costs between git refs. No overlap or ambiguity.

    Naming Consistency4/5

    Both tool names are single verbs ('diff', 'scan'), which is consistent in style. While not a verb_noun pattern, the naming is uniform and intuitive for the domain.

    Tool Count3/5

    With only 2 tools, the server is very focused. This can be appropriate for a narrow utility, but it feels thin for a full server. A few more tools (e.g., pricing config) might improve scope.

    Completeness3/5

    The tools cover two core operations: scanning and diffing. However, there is no tool for managing pricing configurations or listing assumptions, which could be gaps for advanced use.

  • Average 4.4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 12 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is failing

  • 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.

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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?

    No annotations are provided, so the description carries the full burden. It indicates a read-like operation (diff) and describes the output, but does not explicitly state side effects or permissions. Adequate but not exhaustive.

    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, front-loaded with purpose, and includes parameter docs and return type. Every sentence adds value without repetition.

    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 complexity and the presence of an output schema (not shown), the description adequately covers purpose, parameters, and output format. It could include examples or edge cases but is sufficiently complete for an agent.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema coverage is 0%, but the description documents both parameters: base_ref as the base git ref and head_ref as the head ref defaulting to HEAD. This adds essential meaning beyond 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 compares LLM costs between two git refs, specifying it shows added, removed, or changed call sites and cost impact. This distinguishes it from the sibling 'scan' tool.

    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 provides clear context for when to use the tool (to compare costs between refs) but does not explicitly state when not to use it or mention alternatives. Usage is well implied.

    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?

    No annotations are provided, so the description carries the full burden. It details the scanning action, cost estimation, and return format. While it doesn't cover every edge case (e.g., recursion depth or error handling), it provides sufficient behavioral insight for a read-only analysis 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 well-structured: a lead sentence, then details in Args and Returns sections. Every sentence adds value, and the format is easy to parse.

    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 (2 optional params, no annotations), the description covers the core behavior and return type adequately. It could mention recursion or failure modes, but it is sufficient for most use cases.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The schema has 0% description coverage, but the description fully explains both parameters: 'path' (directory/file, default current dir) and 'calls_per_month' (monthly volume, default null implying CLI default of 1000). This adds essential meaning beyond 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 scans a directory for LLM API calls and estimates costs, specifying providers and purpose. This is a specific verb+resource that distinguishes it from the sibling 'diff'.

    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 clearly indicates when to use the tool (scanning directories for LLM calls and cost estimation). However, it does not explicitly mention when not to use it or provide alternatives, which prevents a top score.

    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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