Skip to main content
Glama
aryansingho07

ExpenseTracker

by aryansingho07
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

    Each tool has a clearly distinct purpose: adding an entry, listing entries, and summarizing entries. There is no overlap or ambiguity between them.

    Naming Consistency4/5

    The naming follows a verb_noun pattern with add_expense and list_expenses, but summarize breaks the pattern by omitting the noun. This is a minor inconsistency that does not harm usability.

    Tool Count5/5

    Three tools is well-scoped for a simple expense tracking server. Each tool serves a core function, and the count feels neither bloated nor thin.

    Completeness3/5

    The server supports creating and reading expenses, but lacks update and delete operations. While summarizing adds value, the missing CRUD capabilities represent a notable gap in the tool surface.

  • Average 3.3/5 across 3 of 3 tools scored. Lowest: 2.6/5.

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

    • No community issues in the last 6 months
    • 4 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

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

    MCP servers without a LICENSE cannot be installed.

  • 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

  • Behavior2/5

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

    With no annotations, the description carries the full burden of behavioral disclosure. It states that an entry is added, but does not disclose validation behavior, duplicate handling, side effects, required-field enforcement, or what happens after a successful add.

    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 a single, direct sentence with virtually no filler. It could be considered slightly under-specified, but as far as conciseness and structure go, it is efficient and easy to parse.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For a 5-parameter create operation with no annotations, no output schema, and no schema descriptions, this is not a complete definition. The description misses parameter details, input formats, result/error behavior, and any explicit differentiation from sibling tools.

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

    Parameters1/5

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

    Schema description coverage is 0%, and the description does not compensate by explaining any of the parameters. The agent must rely entirely on parameter names like date, amount, category, subcategory, and note, with no type, format, or semantic guidance.

    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 operation: 'Add a new expense entry to the database.' It uses a specific verb and resource, and the action is distinct from the sibling tools list_expenses and summarize, though it does not explicitly name them.

    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 is provided about when to use this tool versus list_expenses or summarize outside of the obvious 'add' intent. There are no prerequisite conditions, exclusions, or alternative routing hints.

    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 full behavioral burden. It clearly identifies a read operation and the inclusive date-range boundary, but does not mention pagination, ordering, or the returned fields. This is adequate but not rich.

    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 sentence front-loads the verb, resource, and main constraint. There is no filler or redundant restating of the tool name.

    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?

    For a simple two-parameter list tool, the description covers the core action and range semantics. However, the lack of an output schema and absence of return-shape or date-format guidance leaves some ambiguity for an agent invoking the 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 description coverage is 0%, so the description must compensate. It adds the key semantic that the range is inclusive, but it does not specify the expected date format or confirm how each parameter maps beyond their names.

    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 ('List') and resource ('expense entries'), and adds a clear scope (inclusive date range). This distinguishes it from siblings 'add_expense' (create) and 'summarize' (aggregate).

    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 intended use is implied by the name and siblings, but there is no explicit statement about when to choose this over 'summarize' or how it relates to 'add_expense'. No exclusions or alternative routing are provided.

    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 carries the behavioral burden. It discloses that the date range is inclusive, which is useful, but it does not mention whether the operation is read-only, what metrics are returned, or how categories with no expenses are handled.

    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 front-loaded sentence with no redundancy or filler. Every word contributes to the tool's core behavior.

    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?

    For a three-parameter summarization tool, the description gives the essential operation but leaves gaps. With no annotations and no output schema, it should say more about the return shape, optional category behavior, and what 'summarize' actually computes.

    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 schema provides no descriptions for any parameter (0% coverage), so the description must compensate. It adds meaning by connecting category to grouping and start_date/end_date to an inclusive range, but it does not clarify whether category is a filter or required grouping key, nor the expected date format.

    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 states a specific verb ('summarize') and resource ('expenses'), and adds the grouping dimension (by category) and date range scope. This clearly distinguishes it from the sibling tools add_expense and list_expenses.

    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 intended use is implied: summarize rather than add or list expenses. However, there is no explicit guidance about when to prefer this tool over list_expenses or what scenarios it is not suited for, leaving some inference to the agent.

    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

mcp-server MCP server

Copy to your README.md:

Score Badge

mcp-server 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/aryansingho07/mcp-server'

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