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AlphaFold MCP Server

AlphaFold MCP Server

by Augmented-Nature
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

batch_structure_info

Retrieve detailed or summarized structure information for up to 50 proteins at once using UniProt IDs, with output available in JSON or summary formats for streamlined analysis.

Instructions

Get structure information for multiple proteins simultaneously

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": {
      "uniprotIds": "<uniprotIds>"
    },
    "name": "batch_structure_info"
  }
}

Input Schema

NameRequiredDescriptionDefault
formatNoOutput format (default: json)
uniprotIdsYesArray of UniProt accessions (max 50)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.9/5.0
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 but only states what the tool does, not how it behaves. It lacks details on rate limits, authentication needs, error handling, or what 'structure information' entails (e.g., format details, computational cost). This is inadequate for a tool with potential complexity.

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 a single, efficient sentence that front-loads the core purpose with zero wasted words. It directly communicates the tool's function without redundancy or unnecessary elaboration, making it easy for an agent to parse quickly.

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

Completeness2/5

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

Given no annotations and no output schema, the description is incomplete for a batch processing tool. It doesn't explain what 'structure information' includes (e.g., coordinates, metadata), potential limitations (e.g., response size, timeouts), or error scenarios. This leaves significant gaps for an agent to operate effectively.

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 clear documentation for both parameters (uniprotIds array with limits, format enum). The description adds no parameter-specific semantics beyond implying batch processing via 'multiple proteins', which is already evident from the schema. Baseline 3 is appropriate as the schema does the heavy lifting.

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 'Get' and the resource 'structure information for multiple proteins simultaneously', which is specific and actionable. However, it doesn't explicitly distinguish this batch operation from its sibling 'get_structure' (singular) or other structure-related tools, missing full sibling differentiation.

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 'get_structure' (for single proteins) or 'batch_confidence_analysis' (for confidence data). There's no mention of prerequisites, use cases, or exclusions, leaving the agent to infer usage from context alone.

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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