Run Python code in a secure sandbox without local setup. Declare inline dependencies and execute s…
Scored across 1 tool
There is only one tool, so there is no possibility of confusing it with another. Its purpose (executing Python in a sandbox) is unambiguous.
The single name python_execute follows a clear snake_case verb_noun convention. With one tool there are no competing conventions to clash.
A one-tool surface is borderline thin; the core purpose (run Python) is covered, but a sandbox server typically benefits from companion tools like session/reset or file transfer. It is minimal but not unreasonable for a narrowly scoped executor.
Execution with optional PEP 723 requirements covers the primary use case, but there is no lifecycle support such as managing sandbox state, uploading files, or inspecting outputs/errors separately. Agents can work around this by printing results, but the surface is notably narrow.
Run Python in a Pyodide sandbox with optional PEP 723 requirements.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | Python code to execute | |
| context | No | ||
| timeout | No | ||
| requirements | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
While it mentions 'sandbox' indicating isolation, the description fails to disclose critical behavioral traits given the lack of annotations: execution limits (implied by timeout param but not described), state persistence between calls, stdout/stderr capture, error handling, or filesystem access rules.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The single-sentence structure is efficient and front-loaded with the primary action. However, extreme brevity comes at the cost of omitting necessary guidance for a complex code execution tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 4-parameter code execution tool with no output schema or annotations, the description is insufficient. It lacks explanation of return values, error formats, sandbox limitations, and detailed parameter guidance needed for safe and effective invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With only 25% schema coverage, the description partially compensates by referencing 'PEP 723 requirements' which clarifies the requirements parameter's purpose. However, it omits semantics for 'context' (nested object) and 'timeout' despite the schema coverage gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the core action ('Run Python') and specifies the execution environment ('Pyodide sandbox'), which accurately identifies the tool's function. It lacks sibling differentiation, but no siblings exist.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions 'optional PEP 723 requirements' which weakly implies usage for dependency management, but provides no explicit guidance on when to use this versus alternatives, prerequisites, or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
python_executeHosted Python sandbox for package runtime verification: pip install and usable-import evidence.
Execute code in 8 languages (Python, JS, TS, Go, Java, C++, C, Bash) in gVisor sandboxes.
Build Apps and run code in 30 languages — sandboxed, with persistent sessions for agent loops.
Run Python code from natural language prompts, with optional PyPI package install.
Add one secure layer between your agents and this server.
