Skip to main content
Glama

create_protein_from_pdb_id

Generate protein structures from PDB database identifiers to support molecular modeling and computational chemistry workflows.

Instructions

Create a protein from a PDB ID.

Args: name: Name for the protein code: PDB ID code (e.g., '1HCK')

Returns: Dictionary containing protein information

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesName for the protein
codeYesPDB ID code (e.g., '1HCK')

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

No annotations are provided, so the description carries full burden. It states 'Create a protein' which implies a write/mutation operation, but doesn't disclose behavioral traits like whether this requires authentication, what happens on duplicate names/PDB IDs, rate limits, or side effects. The return format is mentioned but lacks detail on structure or errors.

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 concise with a clear purpose statement followed by Args and Returns sections. However, the Args section redundantly repeats schema information without adding value, and the Returns section is vague ('Dictionary containing protein information'). It's front-loaded but includes some waste.

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 that this is a mutation tool with no annotations, 2 parameters, and an output schema exists (implied by 'Has output schema: true'), the description is minimally adequate. It states the purpose and parameters but lacks important context like behavioral transparency, usage guidelines, and detailed return semantics that the output schema might cover.

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?

Schema description coverage is 100%, so the schema already fully documents both parameters. The description repeats the parameter descriptions verbatim from the schema ('Name for the protein', 'PDB ID code') without adding any additional semantic context, examples beyond '1HCK', or constraints. This meets the baseline for high schema coverage.

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 action ('Create a protein') and the resource source ('from a PDB ID'), which is specific and unambiguous. However, it doesn't distinguish this tool from sibling tools like 'upload_protein' or 'retrieve_protein', leaving some ambiguity about when to use this versus those alternatives.

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 like 'upload_protein' or 'retrieve_protein'. It mentions the PDB ID input but doesn't explain prerequisites, use cases, or exclusions. This leaves the agent with minimal context for tool selection.

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/k-yenko/rowan-mcp'

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