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bash20cu

Professional Python MCP Server

by bash20cu

run_terminal

Execute shell commands directly within the MCP server to run development tasks, tests, and scripts, returning output for automation workflows.

Instructions

Executes a shell command and returns stdout and stderr. Example: npm run dev, pytest, etc.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
commandYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('executes a shell command') and output ('returns stdout and stderr'), but lacks critical details such as execution environment, permissions required, potential side effects (e.g., file modifications), error handling, or security implications. This is a significant gap for a tool that interacts with the shell.

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 appropriately sized and front-loaded, with the core purpose stated first and an example provided for clarity. Both sentences earn their place by explaining the tool's function and illustrating usage, though it could be slightly more structured by separating guidelines from examples.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (executing shell commands) and the presence of an output schema (which likely covers return values), the description is moderately complete. It covers the basic action and output but lacks details on behavioral aspects like safety, environment, or error handling. With no annotations and low schema coverage, it should do more to address these gaps for a tool of this nature.

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 description does not add meaning beyond what the input schema provides. The schema has 1 parameter ('command') with 0% description coverage, and the description only implies its use through the example ('npm run dev, pytest, etc.'), without explaining syntax, constraints, or valid values. Since schema_description_coverage is low (<50%), the description fails to compensate adequately, resulting in a baseline score.

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's purpose: 'Executes a shell command and returns stdout and stderr.' It specifies the verb ('executes'), resource ('shell command'), and outcome ('returns stdout and stderr'). However, it doesn't explicitly differentiate from sibling tools like 'read_code' or 'write_code', which might involve similar operations but with different scopes or purposes.

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. It includes an example ('Example: npm run dev, pytest, etc.'), but this only illustrates usage rather than specifying contexts, prerequisites, or exclusions. There is no mention of when to choose this over sibling tools like 'read_code' or 'write_code' for related tasks.

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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