Skip to main content
Glama

list_proteins

Retrieve paginated protein data for molecular design and simulation workflows. Use this tool to access protein dictionaries with page and size parameters.

Instructions

List proteins.

Args: page: Page number (0-indexed) size: Number per page

Returns: List of protein dictionaries

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number (0-indexed)
sizeNoNumber per page

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 full burden. It mentions pagination behavior ('page' and 'size' parameters) and return format ('List of protein dictionaries'), which is helpful. However, it doesn't disclose critical behavioral traits like whether this lists all proteins or filtered subsets, authentication requirements, rate limits, error conditions, or what fields the dictionaries contain. For a read operation with zero annotation coverage, this leaves significant gaps.

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 three short lines. It's front-loaded with the core purpose ('List proteins'), followed by parameter and return details. There's no wasted text, though the parameter explanations are redundant with the schema. The structure is clear but could be more efficient by omitting the schema-duplicate details.

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 (simple list operation), 100% schema coverage, and presence of an output schema (implied by 'Has output schema: true'), the description is minimally adequate. However, it lacks context about what 'proteins' means in this system, how results are ordered, or any limitations. With no annotations, it should provide more behavioral context 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?

Schema description coverage is 100%, with both parameters ('page' and 'size') fully documented in the schema. The description repeats the parameter explanations verbatim from the schema ('Page number (0-indexed)', 'Number per page'), adding no additional meaning. According to the rules, with high schema coverage (>80%), the baseline is 3 even with no param info in the description.

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 'List proteins' which is a clear verb+resource combination, but it's extremely minimal. It doesn't specify what kind of proteins (e.g., all proteins, user's proteins, public proteins) or provide any context about the scope. While it distinguishes from siblings like 'retrieve_protein' (singular) and 'create_protein_from_pdb_id', it lacks the specificity needed for a 4 or 5 score.

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 doesn't mention sibling tools like 'retrieve_protein' (for single proteins) or 'batch_molecule_lookup' (which might overlap). There's no context about prerequisites, filtering capabilities, or typical use cases. The agent must infer usage from the name alone.

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