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get_molecular_template

Generate Python functions to create molecular contexts for constructing few-shot prompts programmatically in the Sutra MCP server.

Instructions

Returns the Python function for creating molecular contexts (Module 02). Use this to programmatically construct few-shot prompts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The handler function for the 'get_molecular_template' tool, decorated with @mcp.tool(). It simply returns the imported MOLECULAR_CONTEXT_FUNC, which is the molecular template string.
    @mcp.tool()
    def get_molecular_template() -> str:
        """
        Returns the Python function for creating molecular contexts (Module 02).
        Use this to programmatically construct few-shot prompts.
        """
        return MOLECULAR_CONTEXT_FUNC
  • The @mcp.tool() decorator registers the get_molecular_template function as an MCP tool.
    @mcp.tool()
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It states the tool returns a Python function, which implies it's likely a read-only operation. However, it doesn't disclose any behavioral traits like whether it requires authentication, has rate limits, or what happens on errors. The description adds some context about the module and usage but lacks comprehensive behavioral details.

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 with only two sentences. The first sentence states the purpose, and the second provides usage guidance. Every word earns its place, and there's no redundancy or fluff. It's front-loaded with the core functionality.

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

Completeness4/5

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

Given the tool has 0 parameters, 100% schema coverage, and an output schema exists, the description is reasonably complete. It explains what the tool does and how to use it. However, for a tool with no annotations, it could benefit from more behavioral context (e.g., error handling, return format details), though the output schema may cover some of this.

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

Parameters4/5

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

The tool has 0 parameters, and schema description coverage is 100%. The description doesn't need to add parameter information. It appropriately focuses on the tool's purpose and usage without unnecessary parameter details, earning a baseline score of 4 for zero-parameter tools.

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: 'Returns the Python function for creating molecular contexts (Module 02).' It specifies the verb ('returns') and resource ('Python function for creating molecular contexts'), and mentions the module context. However, it doesn't explicitly differentiate this tool from its siblings like 'get_cell_protocol' or 'get_protocol_shell', which appear related.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear usage context: 'Use this to programmatically construct few-shot prompts.' This gives a specific application scenario. However, it doesn't mention when NOT to use this tool or explicitly compare it to alternatives among the sibling tools, which would be needed for a perfect score.

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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