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Backend Architect MCP Server

Backend Architect MCP Server

by jemhakdog
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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get_file_instruction

Generate structured system prompts for AI agents to write code according to specific task types and names within backend development workflows.

Instructions

Returns a strict SYSTEM PROMPT for the agent to write the code.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "task_type": "<task_type>",
      "task_name": "<task_name>"
    },
    "name": "get_file_instruction"
  }
}

Input Schema

NameRequiredDescriptionDefault
task_typeYes
task_nameYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.6/5.0
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 mentions returning a 'strict SYSTEM PROMPT' but does not explain what 'strict' entails, any constraints (e.g., format, length), or the response behavior (e.g., error handling). For a tool with no annotations, this leaves significant gaps in understanding its operational traits.

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 a single sentence that is front-loaded and efficient, with no wasted words. It directly states the tool's action and output, making it appropriately sized for its purpose, though it could benefit from more detail to improve clarity.

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 has an output schema (which should cover return values), the description's minimal content is somewhat acceptable. However, with no annotations, 2 undocumented parameters, and vague purpose, it is incomplete for effective use. It meets a baseline but has clear gaps in context and guidance.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 2 parameters with 0% description coverage, and the tool description does not add any meaning beyond the schema. It does not explain what 'task_type' or 'task_name' represent, their expected values, or how they influence the returned prompt. With low schema coverage, the description fails to compensate, leaving parameters undocumented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool 'Returns a strict SYSTEM PROMPT for the agent to write the code,' which specifies a verb ('Returns') and resource ('SYSTEM PROMPT'), but it is vague about what the prompt contains or its exact purpose. It does not clearly differentiate from sibling tools like 'write_component_file' or 'get_next_pending_task,' leaving ambiguity in its role.

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 does not mention prerequisites, context, or exclusions, such as when to choose 'get_file_instruction' over 'write_component_file' or other siblings. This lack of direction makes it unclear in what scenarios this tool is appropriate.

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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