Skip to main content
Glama
anshux1

NitroStack Calculator MCP Server

by anshux1
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    Calculate and convert_temperature have clearly distinct purposes: one performs arithmetic, the other converts between temperature units. There is no overlap or ambiguity between the two tools.

    Naming Consistency5/5

    Both tool names follow a consistent verb-based pattern, with 'calculate' being a bare verb and 'convert_temperature' using verb_noun. The naming is coherent and predictable.

    Tool Count3/5

    With only 2 tools, the server feels thin for a calculator-focused MCP. While the tools are focused, the count is borderline for what could be a richer domain.

    Completeness3/5

    The tool set covers arithmetic and temperature conversion but lacks common calculator features such as scientific functions or other unit conversions. This leaves notable gaps for a general-purpose calculator, though it may suffice for basic needs.

  • Average 2.9/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 35 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under ISC 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?

    With no annotations provided, the description carries the full burden of behavioral disclosure, but it only states the action. It does not mention potential edge cases like division by zero, return value format, error handling, or any side effects, making behavioral transparency weak.

    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, front-loaded sentence with no unnecessary words. It is concise, though slightly underspecified in terms of behavioral details, but for a simple tool this is acceptable.

    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?

    Given there is no output schema, the description should at least imply the return value or behavior, but it does not. The tool is simple, but the description lacks information about result type, error conditions, or any operational nuances, leaving a completeness gap.

    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 has high coverage (100%) with descriptions for all parameters, including the operation enum. The description adds no extra parameter-level detail, so the baseline score of 3 is appropriate.

    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 tool performs basic arithmetic calculations, which is a specific verb+resource. It distinguishes from the sibling tool convert_temperature, though it does not list the supported operations explicitly (those are defined in the schema).

    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?

    The description provides no guidance on when to use this tool versus alternatives, such as convert_temperature. There is no mention of use cases, contexts, or exclusions, leaving the agent to infer usage from the name and schema alone.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior2/5

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

    With no annotations provided, the description carries the full burden of disclosing behavior. It states it can work with file content or direct input, but the schema requires file_name, file_type, and file_content for all calls, creating ambiguity about how direct input is used. It does not describe how the tool parses file content, what happens if both file and direct value are provided, or any edge cases or error behavior.

    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 two concise sentences, directly front-loaded with the verb 'Convert' and the resource. Every sentence earns its place without unnecessary detail, making it efficiently structured.

    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?

    Despite having 6 parameters, 3 required, and no output schema, the description is minimal. It does not explain how direct input works given the mandatory file fields, what the return value looks like, or how to choose between file and direct input. For a tool with this complexity, the description lacks critical usage details.

    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 descriptions cover all 6 parameters (100%), so the baseline is 3. The description adds context about unit abbreviations (C/F) and the file/direct input modes, but does not clarify the relationship between the optional value/from_unit/to_unit and the required file fields, leaving some semantics unexplained beyond the schema.

    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 tool converts temperature units between Celsius and Fahrenheit, using the specific verb 'convert' and resource 'temperature units'. It also mentions file content or direct input as input modes. However, it does not explicitly differentiate from the sibling tool 'calculate', though the temperature focus makes the purpose fairly distinct.

    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?

    The description provides no explicit guidance on when to use this tool versus the sibling 'calculate'. It only implies that temperature conversion is its domain. Additionally, it mentions two input modes (file content and direct input) but does not clarify when to use one over the other, nor does it explain any prerequisites or exclusions.

    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

health-care-mcp MCP server

Copy to your README.md:

Score Badge

health-care-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/anshux1/health-care-mcp'

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