Calculator MCP Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
The two tools have clearly distinct purposes: one for arithmetic calculations and one for temperature conversion. There is no overlap or ambiguity between them.
Naming Consistency5/5Both tool names follow a consistent verb-based pattern: 'calculate' and 'convert_temperature'. Though one is a single verb and the other is verb_noun, the style is coherent and predictable.
Tool Count4/5With only two tools, the server is minimal but appropriate for a focused calculator MCP server. It feels slightly thin but not unreasonable.
Completeness3/5The server covers basic arithmetic and temperature conversion, but lacks other common calculator features such as advanced math functions or general unit conversion. The coverage is adequate for a narrow calculator domain but has notable gaps.
Average 3.1/5 across 2 of 2 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 11 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
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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
- Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description states the tool works 'based on file content or direct input', but the schema requires file_name, file_type, and file_content for all invocations, making direct input impossible without dummy file data. This contradiction is misleading and fails to disclose the actual required behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, short sentence that gets to the point quickly. It could be slightly more explicit about the file/direct input behavior, but overall it is concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description lacks necessary context about the relationship between the file parameters and the temperature conversion parameters, especially given the required fields. There is also no explanation of output format or behavior when both file and direct input are provided, leaving the tool's usage incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All parameters have descriptions, so schema coverage is complete, meeting the baseline. However, the descriptions are minimal, and the file-related parameters are confusing because they are marked required even for 'direct input' and provide no clarity on their purpose or interaction with the value/from_unit/to_unit parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does 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, identifying the specific verb and resource. However, the mention of 'file content or direct input' introduces some ambiguity about the exact mode of operation, slightly detracting from full clarity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 the sibling tools like 'calculate' or 'upload-and-analyze'. The description only explains what the tool does, not the scenarios in which it should be preferred.
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?
No annotations are present, so the description bears full responsibility. It states it performs calculations but omits potential side effects like division-by-zero errors or whether the tool is read-only, leaving behavior ambiguous.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise, using only four words to convey the entire purpose. There is no redundancy or unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description lacks essential context such as the return format, error handling, or precision of results. Given no output schema and no annotations, the tool is incomplete for an agent to fully understand behavior without additional inference.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema descriptions cover all parameters, giving a baseline of 3. The tool description itself adds no extra meaning beyond the schema, but the schema already defines 'a', 'b', and 'operation' sufficiently for basic arithmetic.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does 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 action. It distinguishes itself from sibling tools like convert_temperature by being generic, though it lacks detail on exact operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5Does 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 typical use cases or conditions for selection.
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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