Skip to main content
Glama
bash20cu

Professional Python MCP Server

by bash20cu

read_code

Reads file contents to access code and documentation in Python, TypeScript, JavaScript, JSON, and Markdown formats for development workflows.

Instructions

Reads the content of a file. Supported extensions: .py, .ts, .tsx, .js, .json, .md.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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 mentions supported file extensions, which adds useful context beyond the schema, but fails to address critical behaviors like error handling (e.g., what happens if the file doesn't exist or has an unsupported extension), permissions required, or rate limits. The description doesn't contradict annotations (none exist).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise—two sentences that directly state the tool's function and constraints without any fluff. It is front-loaded with the core purpose, and every sentence adds value (the extension list is necessary context).

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 low complexity (one parameter) and the presence of an output schema (which handles return values), the description is minimally adequate. However, it lacks details on behavioral aspects like error handling or permissions, which are important for a file-read operation. With no annotations, it should do more to be fully complete.

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 schema description coverage is 0%, so the description must compensate. It adds meaning by specifying supported file extensions, which clarifies the 'path' parameter's expected content. However, it doesn't explain the path format (e.g., absolute vs. relative), constraints, or examples. With one parameter and partial compensation, a baseline 3 is appropriate.

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 verb ('Reads') and resource ('content of a file'), making the purpose immediately understandable. It distinguishes from siblings like 'write_code' by specifying it's a read operation, though it doesn't explicitly contrast with other read-like tools (none exist in the sibling list).

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 mentions supported file extensions but doesn't explain when to choose this over other tools (e.g., 'run_terminal' for executing code or 'list_models_with_limits' for other operations). No exclusions or prerequisites are stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/bash20cu/mcp-server-python'

If you have feedback or need assistance with the MCP directory API, please join our Discord server