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mayankchugh-learning

expense-mcp

by mayankchugh-learning
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a distinct purpose: adding an expense, listing expenses by date range, and summarizing spending by category. There is no overlap or ambiguity.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern using snake_case (add_expense, list_expenses, summarize). The pattern is predictable and clear.

    Tool Count5/5

    With 3 tools, the server is well-scoped for its purpose of basic expense tracking. The count is neither too few nor too many for the apparent domain.

    Completeness4/5

    The tool surface covers adding, listing, and summarizing expenses, which covers core use cases. However, it lacks update and delete operations, which are minor gaps for complete lifecycle management.

  • Average 4.4/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 0 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.

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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

  • Behavior4/5

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

    The description discloses the return value behavior, including the possibility of None. It also provides a date format hint. However, it does not mention any side effects, authentication needs, or rate limits, though for a simple insert tool this is adequate. Annotations are missing, so the description carries full burden.

    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 well-structured with 'Args' and 'Returns' sections. Every sentence adds value, and there is no redundant or extraneous text. It is appropriately sized for the tool's complexity.

    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?

    The description covers all necessary information: what the tool does, how to use each parameter, and what to expect as output. It is complete for a tool of this complexity, especially given the presence of an output schema (even if not provided in the input, the description explains it).

    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?

    With 0% schema description coverage, the description fully compensates by providing semantic meaning for each parameter: date format, amount as positive monetary value, category as high-level label, and optional fields with defaults. This exceeds the bare schema definition.

    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 verb 'Insert', the resource 'expense row', and the return value 'new row id'. It distinguishes itself from sibling tools 'list_expenses' and 'summarize' by being the only write operation.

    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 for adding an expense, but does not provide explicit guidance on when to use this tool versus alternatives, nor does it mention any prerequisites or when not to use.

    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 full burden. It discloses the return format (list of dicts with category and total_amount, sorted) and the filtering capability. However, it does not explicitly state that the tool is read-only or non-destructive, which would be helpful.

    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, with clear Args and Returns sections. Every sentence serves a purpose, and the most important information is front-loaded.

    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?

    Given the tool's simplicity and the presence of an output schema (implied by context), the description adequately covers purpose, parameters, and return value. No critical gaps are evident for a basic aggregation tool.

    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 input schema has 0% description coverage, so the description fully compensates by explaining each parameter: start_date and end_date with recommended format YYYY-MM-DD, and category as an optional filter. This adds significant 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 aggregates total spend per category over a date range, using a specific verb and resource. It differentiates itself from siblings (add_expense, list_expenses) by focusing on aggregation rather than individual entries.

    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 explains what the tool does but does not provide explicit guidance on when to use it over alternatives like list_expenses. It implies that it is suitable for summarized data, but lacks when-not or contrast with siblings.

    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 provided, so description carries full burden. It discloses ordering (by id ascending), inclusive date range, and return structure (list of dicts with specific keys). Does not mention permissions, rate limits, or pagination, but for a simple read operation this is adequate.

    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?

    Description is concise with no wasted words. It uses clear sectioning (Args, Returns) and front-loads the core purpose in the first sentence.

    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?

    Given the tool's simplicity and presence of an output schema (contextual signal), the description fully covers behavior: filtering, ordering, and return format. No gaps for an agent to misuse the tool.

    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?

    Schema coverage is 0%, so description compensates by explaining start_date and end_date as range bounds with recommended format 'YYYY-MM-DD'. This adds meaning beyond the schema's simple type declaration.

    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 expenses filtered by date range, ordered by id ascending. It distinguishes itself from sibling tools 'add_expense' (adds) and 'summarize' (aggregates) by focusing on filtering and listing.

    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 gives clear context about date range filtering and order, and implies listing over adding/summarizing. However, no explicit when-not-to-use or alternative tool names are provided.

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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