SymPy Sandbox MCP
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'sympy' has a clear and distinct purpose: executing Python/SymPy math code within a sandboxed environment.
Naming Consistency5/5Since there is only one tool, naming consistency is inherently perfect. The tool name 'sympy' is straightforward and matches the server's purpose, with no other tools to compare against for patterns.
Tool Count2/5A single tool is too few for the apparent scope of a SymPy sandbox, which could benefit from more granular operations like simplify, solve, or differentiate. This minimal set may force agents to bundle multiple steps into one call, reducing flexibility and increasing error risk.
Completeness2/5The tool surface is severely incomplete for mathematical computation. While the single tool can execute arbitrary SymPy code, it lacks dedicated tools for common operations (e.g., simplification, solving equations, calculus), making it harder for agents to reliably perform structured tasks without manual coding in each call.
Average 4.9/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit 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
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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
- Behavior5/5
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. It thoroughly describes safety boundaries (blocked system calls, file I/O, network access), execution constraints (must print() results), and error conditions with retry strategies, offering rich behavioral context beyond basic functionality.
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 well-structured with clear sections (safety boundaries, input rules, workflow, retry guidance) and uses bullet points for readability. It is appropriately sized for the tool's complexity, though some sentences could be slightly more concise (e.g., the workflow steps are detailed but not overly verbose).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (sandboxed code execution), lack of annotations, and low schema coverage, the description is highly complete. It covers purpose, usage, safety, parameters, workflow, and error handling. The presence of an output schema means return values need not be explained, and the description addresses all other critical aspects thoroughly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage for its single parameter 'code', but the description compensates fully by explaining that 'code' is a string containing Python/SymPy math code, detailing input rules (single argument, must print()), and providing workflow examples. It adds significant meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as executing Python/SymPy math code in a sandbox environment. It specifies the exact functionality (execute code), the domain (math/SymPy), and the context (sandbox with safety boundaries), making it highly specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when and how to use the tool, including a recommended workflow (steps 1-4), input rules (single code argument, use print()), and retry guidance for specific errors (E_AST_BLOCK, E_TIMEOUT, E_MEMORY). It comprehensively covers usage scenarios and error handling.
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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