Skip to main content
Glama
doudou-fly

Calculator MCP Server

by doudou-fly

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool performs a unique arithmetic operation (addition, subtraction, multiplication, division) with no overlap. The distinct mathematical symbols in descriptions remove any ambiguity.

    Naming Consistency5/5

    All tool names follow the same verb pattern using the operation name (add, subtract, multiply, divide). This is perfectly consistent and predictable.

    Tool Count5/5

    Four tools is an ideal scope for a basic calculator server, covering the core arithmetic operations without unnecessary bloat.

    Completeness4/5

    The four basic operations cover the primary use case for a calculator. A modulo or power operation could be added, but the surface is not severely incomplete.

  • Average 3.8/5 across 4 of 4 tools scored.

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

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

    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?

    No annotations exist, so the description must carry full behavioral disclosure. It only restates the operation ('calculate product') without detailing return type, side effects, or error handling, leaving the agent to infer these from the name and schema.

    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, concise sentence in Chinese with no redundant words. It is perfectly front-loaded and 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?

    For a trivial arithmetic tool with fully documented parameters, the description is adequate. No output schema exists, but the return value (product) is strongly implied by the operation, so no significant gap is present.

    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 covers 100% of parameters with clear descriptions (第一个乘数, 第二个乘数). The tool description adds no additional semantic value beyond what the schema already provides, so baseline 3 is appropriate.

    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 (计算) and resource (两个数的积), clearly distinguishing it from sister operations like add, subtract, and divide. It directly states what the tool does 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?

    No explicit usage guidance or alternative comparisons are provided. However, the name and description make the use case obvious for a basic arithmetic operation, so usage is reasonably implied.

    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 should disclose behavioral traits. It states the operation but does not mention error handling for division by zero, potential precision issues, or return value expectations. The schema does note that b cannot be zero, but the description itself adds no behavioral context beyond the operation.

    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 short sentence, directly stating the operation with no superfluous words. It is well-structured and front-loaded.

    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 simple arithmetic tool with two parameters, the description and schema together are largely sufficient. It could optionally mention error behavior for b=0, but the schema already indicates the constraint, so the description is reasonably complete.

    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 provides full descriptions for both parameters (a as dividend, b as divisor, with constraint b cannot be zero). The description only restates the operation and does not add additional parameter meaning beyond what the schema already covers.

    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 computes the quotient of two numbers (a ÷ b), using a specific verb ('calculate') and resource ('quotient'). This unambiguously distinguishes it from sibling tools like add, subtract, and multiply.

    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 the tool is used when division is needed, but it does not explicitly state when to use it over alternatives or mention any exclusions. No contextual guidance is provided beyond the operation itself.

    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 must carry the behavioral burden. It defines the order-sensitive subtraction behavior (a - b), which is useful. However, it does not disclose error handling, return type, or behavior for edge cases, though the schema covers input types.

    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, front-loaded sentence conveys the core operation and formula without redundancy. Every word contributes to understanding the tool's purpose.

    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 simple two-number arithmetic tool, the description plus schema sufficiently covers the operation and parameters. It lacks explicit return-type or error information, but the low complexity and full parameter coverage make the tool adequately specified.

    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 input schema already provides descriptions for both parameters ('被减数' minuend and '减数' subtrahend) with 100% coverage. The description adds the explicit formula a - b, which reinforces the relational meaning of the parameters.

    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 explicitly states the operation: '计算两个数的差 (a - b)', clearly indicating subtraction of b from a. This distinguishes it from sibling tools add, multiply, and divide.

    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 on when to use this tool versus alternatives. The description only states the mathematical operation and does not mention exclusions, prerequisites, or when a different arithmetic tool would be appropriate.

    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 of behavioral disclosure. The description clearly states the pure computation of a sum, which is the primary behavior. It does not mention edge cases or return type, but for such a trivial operation, the disclosed behavior is adequate and not misleading.

    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 sentence that is front-loaded and directly states the operation. Every word earns its place, and there is zero waste.

    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 simple two-number addition tool with complete parameter schema, the description sufficiently covers the tool's purpose. It doesn't explicitly state the return value, but that is self-evident from the operation. The low complexity and high schema coverage make the description complete enough.

    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 100%, with both parameters 'a' and 'b' described as addends. The description does not add any extra meaning beyond the schema, so the baseline score of 3 applies.

    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 '计算两个数的和' clearly states the tool computes the sum of two numbers. It uses a specific verb and resource, and naturally distinguishes itself from siblings subtract, multiply, and divide by describing the addition 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 use when addition is needed, but it does not explicitly state when to use or not use this tool versus the sibling operations. The context is clear from the name and sibling names, but explicit guidance is absent, so it falls to the 'implied usage' level.

    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

open-mcp MCP server

Copy to your README.md:

Score Badge

open-mcp 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/doudou-fly/open-mcp'

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