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yash-201

Expense Tracker MCP Server

by yash-201
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have completely disjoint purposes—one rolls dice and the other adds numbers. There is no ambiguity between them and no chance of an agent misselecting one for the other.

    Naming Consistency5/5

    Both tool names follow the same verb_noun snake_case pattern: roll_dice and add_numbers. The naming is predictable and straightforward.

    Tool Count1/5

    A server named 'Expense Tracker' offers only two tools that perform unrelated operations (dice rolling and arithmetic). This is a severe mismatch between scope and purpose, making the count completely inappropriate.

    Completeness1/5

    The tools do not cover any expense tracking functionality whatsoever—no add expense, list expenses, delete expense, or summaries. The surface is entirely incomplete for the stated domain.

  • Average 3.8/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit 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

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  • 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

  • Behavior2/5

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

    No annotations are provided, and the description does not disclose any behavioral details beyond the addition itself. It does not mention whether the operation is pure, what it returns, how errors are handled, or any side effects. 'Add' implies a mathematical operation, but the description lacks the contextual depth needed to fully inform an agent.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

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

    The description is extremely short and to the point, consisting of a single sentence with no filler. The word 'together' is slightly redundant with 'add', but overall it is efficient and front-loaded. For a tool of this simplicity, this level of conciseness is appropriate.

    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 the minimal text, the tool is simple enough that the description covers the core behavior, and the presence of an output schema means return values do not need to be spelled out. However, some guidance on usage context or exception cases would have been beneficial, so it is not the maximum possible.

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

    Parameters2/5

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

    The schema only specifies that 'a' and 'b' are numbers, with 0% schema description coverage. The description merely repeats the existence of two numbers without adding any semantics about the role of each parameter, order, precision, or potential edge cases. Since addition is commutative, the lack of order explanation is less problematic, but the tool still adds no value 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 uses a specific verb ('add') and clearly identifies the resource ('two numbers'), leaving no ambiguity about what the tool does. The sibling tool 'roll_dice' is completely unrelated to arithmetic, so an agent can instantly distinguish this tool from siblings without further reading.

    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 is given about when to use this tool versus alternatives, and there are no exclusions or prerequisites. However, the simple, conventional nature of addition and the unrelated sibling make the intended usage implicit.

    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 carries the behavioral burden. It communicates a stateless operation that returns results, but it does not explicitly disclose nondeterminism, bounds on n_dice, or any edge-case behavior beyond what is obvious from rolling dice.

    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?

    A single efficient sentence that conveys the action, parameter meaning, die type, and return behavior with no wasted words. It is front-loaded and easy to parse.

    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 tool with one parameter and an output schema, the description covers the essential invocation semantics. The output schema covers return details, so the description need not restate them.

    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 schema has no parameter descriptions, so the tool description must clarify the meaning of n_dice. 'Roll n_dice 6-sided dice' clearly maps the sole parameter to the number of dice. It could also mention the default, but that is already present 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 a specific verb and resource: rolling n_dice six-sided dice and returning results. It is immediately distinguishable from the sibling tool add_numbers, so an agent can select it correctly without ambiguity.

    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 expected use is implied: use this tool when dice rolls are needed. However, the description does not explicitly state when to use it versus add_numbers, nor does it provide exclusions or prerequisites.

    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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