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The system registers a 'calculate' tool within the MCP framework that accepts string expressions and returns computed results, enabling LLM clients to invoke calculations through a protocol-agnostic communication layer rather than direct function calls. FastMCP framework handles protocol marshaling, request routing, and response serialization automatically.","intents":["I want my LLM agent to perform precise numerical calculations without hallucinating math","I need to integrate a calculator tool into my MCP-compatible LLM application","I want to expose calculation capabilities to multiple LLM clients through a standardized protocol"],"best_for":["LLM application developers building agents with MCP support","Teams integrating calculator functionality into Claude, other MCP-aware LLMs","Developers needing protocol-agnostic tool exposure across multiple LLM platforms"],"limitations":["Requires MCP client implementation — not compatible with non-MCP LLM APIs (e.g., direct OpenAI API calls)","Single-threaded expression evaluation — concurrent requests queue sequentially","No built-in result caching — identical expressions re-evaluated on each invocation"],"requires":["Python 3.10+","mcp>=1.4.1 library","MCP-compatible client (Claude, custom MCP client, etc.)","uvx or pip for installation"],"input_types":["string (mathematical expression)"],"output_types":["numeric result (float/int)","error message (string)"],"categories":["tool-use-integration","mcp-protocol"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-calculator__cap_1","uri":"capability://safety.moderation.secure.expression.evaluation.with.sandboxed.math.function.execution","name":"secure expression evaluation with sandboxed math function execution","description":"Evaluates mathematical expressions in a restricted execution environment that whitelists only safe mathematical functions (arithmetic operators, trigonometry, logarithms, etc.) while blocking dangerous operations like file I/O, system calls, or arbitrary code execution. The expression evaluator uses a security model that validates input syntax before execution and restricts the namespace available to eval() to a curated set of math functions from Python's math module, preventing injection attacks and unintended side effects.","intents":["I want to safely evaluate user-provided math expressions without risking code injection","I need to prevent LLM-generated expressions from accessing the filesystem or system","I want mathematical evaluation with guaranteed termination and no side effects"],"best_for":["Production LLM applications requiring sandboxed calculation","Multi-tenant systems where expression isolation is critical","Applications processing untrusted mathematical expressions from LLMs"],"limitations":["Restricted function namespace — only standard math module functions available (no numpy, scipy, custom functions)","No variable persistence — each expression evaluation starts with clean namespace","Expression complexity limits — deeply nested or extremely long expressions may timeout","No symbolic math — purely numerical evaluation (e.g., cannot simplify algebraic expressions)"],"requires":["Python 3.10+","math module (standard library)","mcp>=1.4.1"],"input_types":["string (mathematical expression with operators: +, -, *, /, **, %, and functions: sin, cos, tan, sqrt, log, etc.)"],"output_types":["float (computed result)","error message (string describing syntax or evaluation failure)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-calculator__cap_2","uri":"capability://data.processing.analysis.mathematical.function.library.exposure.with.standard.math.module.bindings","name":"mathematical function library exposure with standard math module bindings","description":"Provides access to Python's standard math module functions (trigonometric: sin, cos, tan; logarithmic: log, log10, log2; exponential: exp, sqrt; constants: pi, e; and others) through the sandboxed expression evaluator. These functions are pre-imported into the evaluation namespace, allowing expressions like 'sin(pi/2)' or 'sqrt(16)' to execute without explicit imports. The binding is static — the set of available functions is fixed at server startup and cannot be extended at runtime.","intents":["I want my LLM to perform trigonometric, logarithmic, and exponential calculations","I need access to mathematical constants like pi and e in expressions","I want standard mathematical functions available without custom implementation"],"best_for":["Scientific and engineering LLM applications","Data analysis workflows requiring standard math operations","Educational tools demonstrating mathematical concepts"],"limitations":["No custom function definition — cannot add user-defined functions at runtime","No advanced math libraries — numpy, scipy, sympy not available","Limited to Python's math module scope — no complex number support, no matrix operations","No function composition helpers — must write full nested expressions"],"requires":["Python 3.10+","math module (standard library, included with Python)"],"input_types":["string expressions using math functions: sin, cos, tan, sqrt, log, exp, factorial, etc."],"output_types":["float (numerical result)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-calculator__cap_3","uri":"capability://safety.moderation.structured.error.reporting.with.expression.syntax.validation","name":"structured error reporting with expression syntax validation","description":"Validates mathematical expressions for syntax errors before execution and returns detailed error messages when evaluation fails. The system catches exceptions during expression evaluation (SyntaxError, NameError, TypeError, ZeroDivisionError, etc.) and returns human-readable error descriptions to the LLM client, enabling the LLM to correct malformed expressions and retry. Error messages include the type of error and context about what went wrong, facilitating debugging of LLM-generated expressions.","intents":["I want clear error messages when my LLM generates invalid math expressions","I need to help the LLM understand why an expression failed so it can self-correct","I want to distinguish between syntax errors, undefined functions, and runtime errors"],"best_for":["LLM agents with self-correction loops","Interactive applications where users/LLMs need feedback on expression validity","Debugging workflows for LLM-generated mathematical expressions"],"limitations":["Error messages are Python-specific — may confuse non-technical LLM users","No expression suggestions — does not propose corrections, only reports failures","Limited context in error messages — does not highlight the exact position of syntax errors","No expression pre-compilation — syntax validation happens at evaluation time, not parse time"],"requires":["Python 3.10+","mcp>=1.4.1"],"input_types":["string (mathematical expression)"],"output_types":["error message (string describing the failure type and context)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-calculator__cap_4","uri":"capability://tool.use.integration.standalone.mcp.server.process.with.uvx.pip.deployment","name":"standalone mcp server process with uvx/pip deployment","description":"Packages the calculator as a deployable MCP server that runs as an independent process communicating with MCP clients via JSON-RPC over stdio or network sockets. Supports two installation methods: uvx (direct execution without local installation) and pip (traditional Python package installation). The server bootstraps via a main() entry point that initializes the FastMCP framework, registers the calculate tool, and enters the MCP protocol event loop, handling incoming client requests until shutdown.","intents":["I want to deploy a calculator tool that my MCP client can discover and invoke","I need to run the calculator as a separate process without modifying my LLM application code","I want to install and update the calculator independently from my main application"],"best_for":["Developers integrating MCP tools into Claude or other MCP-aware LLMs","Teams managing multiple MCP servers in a microservices architecture","Users wanting zero-dependency tool deployment via uvx"],"limitations":["Requires separate process management — no in-process embedding","Inter-process communication overhead — JSON-RPC serialization adds latency (~10-50ms per call)","No built-in process monitoring — requires external supervisor (systemd, Docker, etc.) for production","Single-instance deployment — no built-in load balancing or clustering"],"requires":["Python 3.10+","uvx (for uvx deployment) OR pip (for traditional installation)","mcp>=1.4.1","MCP client configured to launch the server process"],"input_types":["MCP protocol requests (JSON-RPC)"],"output_types":["MCP protocol responses (JSON-RPC)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-calculator__cap_5","uri":"capability://automation.workflow.cross.platform.mcp.server.execution.with.minimal.dependencies","name":"cross-platform mcp server execution with minimal dependencies","description":"Runs on Linux, macOS, and Windows with only Python 3.10+ and the mcp library as runtime dependencies, requiring no system-specific compilation or platform-specific code paths. The codebase uses only standard library modules (math, json, sys) and the mcp framework, avoiding heavy dependencies like numpy or scipy that require compilation. This minimal dependency footprint enables rapid deployment across heterogeneous environments and reduces supply chain risk.","intents":["I want to deploy the calculator on multiple operating systems without recompilation","I need a lightweight tool that doesn't require heavy scientific libraries","I want to minimize dependency management overhead in my MCP infrastructure"],"best_for":["Teams with heterogeneous infrastructure (Linux servers, macOS development, Windows clients)","Environments with strict dependency policies or supply chain security requirements","Rapid prototyping where minimal setup time is critical"],"limitations":["No advanced math capabilities — limited to Python's standard math module","No performance optimization for large-scale calculations — pure Python execution","No GPU acceleration — all computation on CPU","Limited to Python ecosystem — no C/Rust optimizations"],"requires":["Python 3.10+ (available on Linux, macOS, Windows)","mcp>=1.4.1 library"],"input_types":["mathematical expressions (string)"],"output_types":["numerical results (float)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["Python 3.10+","mcp>=1.4.1 library","MCP-compatible client (Claude, custom MCP client, etc.)","uvx or pip for installation","math module (standard library)","mcp>=1.4.1","math module (standard library, included with Python)","uvx (for uvx deployment) OR pip (for traditional installation)","MCP client configured to launch the server process","Python 3.10+ (available on Linux, macOS, Windows)"],"failure_modes":["Requires MCP client implementation — not compatible with non-MCP LLM APIs (e.g., direct OpenAI API calls)","Single-threaded expression evaluation — concurrent requests queue sequentially","No built-in result caching — identical expressions re-evaluated on each invocation","Restricted function namespace — only standard math module functions available (no numpy, scipy, custom functions)","No variable persistence — each expression evaluation starts with clean namespace","Expression complexity limits — deeply nested or extremely long expressions may timeout","No symbolic math — purely numerical evaluation (e.g., cannot simplify algebraic expressions)","No custom function definition — cannot add user-defined functions at runtime","No advanced math libraries — numpy, scipy, sympy not available","Limited to Python's math module scope — no complex number support, no matrix operations","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.22,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:02.371Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=calculator","compare_url":"https://unfragile.ai/compare?artifact=calculator"}},"signature":"wTSa1+r5+viAGtw8rVGZrARrN0mFCPrAU1cQ/kkzSmXdbW6af8aZK7BgJRalbC73KTkH6hkUYRIiqrcNXw1iAQ==","signedAt":"2026-06-21T11:58:43.879Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/calculator","artifact":"https://unfragile.ai/calculator","verify":"https://unfragile.ai/api/v1/verify?slug=calculator","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}