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AlphaFold MCP Server

AlphaFold MCP Server

by Augmented-Nature
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

batch_confidence_analysis

Evaluate confidence scores for up to 30 protein structures using UniProt accessions. Gain insights into prediction reliability with batch processing on the AlphaFold MCP Server.

Instructions

Analyze confidence scores for multiple proteins

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_confidence_analysis"
  }
}

Input Schema

NameRequiredDescriptionDefault
uniprotIdsYesArray of UniProt accessions (max 30)

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 offers minimal insight. It mentions analyzing confidence scores but doesn't describe the analysis process, output format, rate limits, or potential side effects. This leaves the agent with insufficient context for safe and effective use.

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, clear sentence with no wasted words. It's front-loaded with the core purpose, making it easy to parse quickly. This efficiency is appropriate for a tool with a straightforward schema and no complex behavioral nuances to explain.

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 the lack of annotations and output schema, the description is incomplete for effective tool use. It doesn't explain what the analysis entails, the format or content of results, or how it differs from similar tools. For a batch analysis tool in a domain with many siblings, more context is needed to guide the agent.

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?

The description adds no parameter-specific information beyond what's in the schema, which has 100% coverage. The schema fully documents the 'uniprotIds' parameter, including its type, constraints, and description. Thus, the description meets the baseline but doesn't enhance understanding of parameter usage or semantics.

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 ('Analyze') and target resource ('confidence scores for multiple proteins'), making the purpose understandable. It distinguishes from siblings like 'get_confidence_scores' by specifying batch/multiple analysis, though it could be more explicit about what 'analyze' entails versus 'get'.

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?

No guidance is provided on when to use this tool versus alternatives like 'get_confidence_scores' or 'analyze_confidence_regions'. The description implies batch processing but doesn't specify scenarios where batch analysis is preferred over individual queries or other sibling tools.

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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