Used for installation and setup of the AlphaFold MCP server components
Mentioned as part of the development requirements for contributing to the server codebase
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@AlphaFold MCP Server get the AlphaFold structure for human p53 protein"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A comprehensive Model Context Protocol (MCP) server that provides access to the AlphaFold Protein Structure Database through a rich set of tools and resources for protein structure prediction analysis.
This MCP server enables seamless integration with AlphaFold's vast collection of protein structure predictions, offering tools for structure retrieval, confidence analysis, batch processing, and visualization preparation. Perfect for researchers, bioinformaticians, and structural biologists working with predicted protein structures.
Related MCP server: UniProt MCP Server
Structure Retrieval: Get AlphaFold predictions by UniProt ID
Multi-format Downloads: Support for PDB, CIF, BCIF, and JSON formats
Availability Checking: Verify if predictions exist for specific proteins
Structure Search: Find proteins by name, gene, or organism
Organism Browsing: List all available structures for specific species
Coverage Statistics: Get comprehensive organism-level statistics
Per-residue Confidence: Detailed confidence scores for each amino acid
Region Analysis: Identify high/low confidence structural regions
Quality Validation: Assess overall prediction reliability
Bulk Retrieval: Process multiple proteins simultaneously
Batch Downloads: Efficient multi-structure downloads
Parallel Analysis: Confidence analysis for protein sets
Structure Comparison: Side-by-side analysis of multiple proteins
Similarity Search: Find structurally related proteins
Coverage Comparison: Analyze prediction completeness
PyMOL Scripts: Ready-to-use visualization scripts
ChimeraX Integration: Confidence-colored structure viewing
Custom Export Formats: Flexible data export options
# Clone or create the server directory
npm install
# Build the server
npm run buildAdd to your MCP configuration:
{
"mcpServers": {
"alphafold-server": {
"command": "node",
"args": ["/path/to/alphafold-server/build/index.js"]
}
}
}# Start the server
npm start
# Or run directly
node build/index.jsget_structureRetrieve AlphaFold structure prediction for a specific UniProt ID.
Parameters:
uniprotId (required): UniProt accession (e.g., "P21359", "Q8N726")
format (optional): Output format - "pdb", "cif", "bcif", or "json" (default: "json")
Example:
{
"uniprotId": "P04637",
"format": "json"
}download_structureDownload AlphaFold structure file in specified format.
Parameters:
uniprotId (required): UniProt accession
format (optional): File format - "pdb", "cif", or "bcif" (default: "pdb")
check_availabilityCheck if AlphaFold structure prediction is available for a UniProt ID.
Parameters:
uniprotId (required): UniProt accession to check
search_structuresSearch for available AlphaFold structures by protein name or gene.
Parameters:
query (required): Search term (protein name, gene name, etc.)
organism (optional): Filter by organism
size (optional): Number of results (1-100, default: 25)
list_by_organismList all available structures for a specific organism.
Parameters:
organism (required): Organism name (e.g., "Homo sapiens", "Escherichia coli")
size (optional): Number of results (1-100, default: 50)
get_organism_statsGet statistics about AlphaFold coverage for an organism.
Parameters:
organism (required): Organism name
get_confidence_scoresGet per-residue confidence scores for a structure prediction.
Parameters:
uniprotId (required): UniProt accession
threshold (optional): Confidence threshold (0-100)
analyze_confidence_regionsAnalyze confidence score distribution and identify high/low confidence regions.
Parameters:
uniprotId (required): UniProt accession
get_prediction_metadataGet metadata about the prediction including version, date, and quality metrics.
Parameters:
uniprotId (required): UniProt accession
batch_structure_infoGet structure information for multiple proteins simultaneously.
Parameters:
uniprotIds (required): Array of UniProt accessions (max 50)
format (optional): Output format - "json" or "summary" (default: "json")
batch_downloadDownload multiple structure files.
Parameters:
uniprotIds (required): Array of UniProt accessions (max 20)
format (optional): File format - "pdb" or "cif" (default: "pdb")
batch_confidence_analysisAnalyze confidence scores for multiple proteins.
Parameters:
uniprotIds (required): Array of UniProt accessions (max 30)
compare_structuresCompare multiple AlphaFold structures for analysis.
Parameters:
uniprotIds (required): Array of UniProt accessions to compare (2-10)
find_similar_structuresFind AlphaFold structures similar to a given protein.
Parameters:
uniprotId (required): Reference UniProt accession
organism (optional): Filter by organism
get_coverage_infoGet information about sequence coverage in the AlphaFold prediction.
Parameters:
uniprotId (required): UniProt accession
validate_structure_qualityValidate and assess the overall quality of an AlphaFold prediction.
Parameters:
uniprotId (required): UniProt accession
export_for_pymolExport structure data formatted for PyMOL visualization.
Parameters:
uniprotId (required): UniProt accession
includeConfidence (optional): Include confidence score coloring (default: true)
export_for_chimeraxExport structure data formatted for ChimeraX visualization.
Parameters:
uniprotId (required): UniProt accession
includeConfidence (optional): Include confidence score coloring (default: true)
get_api_statusCheck AlphaFold API status and database statistics.
Parameters: None
alphafold://structure/{uniprotId}MIME Type: application/json
Description: Complete AlphaFold structure prediction for a UniProt ID
alphafold://pdb/{uniprotId}MIME Type: chemical/x-pdb
Description: PDB format structure file for a UniProt ID
alphafold://confidence/{uniprotId}MIME Type: application/json
Description: Per-residue confidence scores for a structure prediction
alphafold://summary/{organism}MIME Type: application/json
Description: Summary of all available structures for an organism
// 1. Check if structure is available
await use_mcp_tool("alphafold-server", "check_availability", {
uniprotId: "P04637",
});
// 2. Get structure metadata
await use_mcp_tool("alphafold-server", "get_prediction_metadata", {
uniprotId: "P04637",
});
// 3. Analyze confidence scores
await use_mcp_tool("alphafold-server", "get_confidence_scores", {
uniprotId: "P04637",
threshold: 70,
});// Compare multiple related proteins
await use_mcp_tool("alphafold-server", "compare_structures", {
uniprotIds: ["P04637", "P53350", "P63151"],
});
// Batch confidence analysis
await use_mcp_tool("alphafold-server", "batch_confidence_analysis", {
uniprotIds: ["P04637", "P53350", "P63151"],
});// Export for PyMOL with confidence coloring
await use_mcp_tool("alphafold-server", "export_for_pymol", {
uniprotId: "P04637",
includeConfidence: true,
});
// Export for ChimeraX
await use_mcp_tool("alphafold-server", "export_for_chimerax", {
uniprotId: "P04637",
includeConfidence: true,
});// Get human protein statistics
await use_mcp_tool("alphafold-server", "get_organism_stats", {
organism: "Homo sapiens",
});
// List available structures
await use_mcp_tool("alphafold-server", "list_by_organism", {
organism: "Homo sapiens",
size: 100,
});The server connects to the AlphaFold API at https://alphafold.ebi.ac.uk/api/ and provides structured access to:
Structure Predictions: Complete protein structure data
Confidence Scores: Per-residue reliability metrics
Metadata: Prediction versions, dates, and quality information
Cross-references: Links to other databases and resources
The server includes comprehensive error handling for:
Invalid UniProt IDs
Missing structure predictions
API connectivity issues
Rate limiting and timeouts
Malformed requests
Please be mindful of API usage:
Batch operations are limited to reasonable sizes
Large requests are automatically chunked
Built-in delays prevent API overload
Contributions are welcome! Please ensure:
TypeScript type safety
Comprehensive error handling
Documentation for new features
Testing with real AlphaFold data
Developed by Augmented Nature - augmentednature.ai
Augmented Nature specializes in creating AI-powered tools and infrastructure for scientific research and data analysis.
MIT License - see LICENSE file for details.
If you use this MCP server in your research, please cite:
AlphaFold Database: https://alphafold.ebi.ac.uk/
Model Context Protocol: https://modelcontextprotocol.io/
For issues and questions:
Check the AlphaFold API documentation
Review error messages for debugging hints
Ensure UniProt IDs are valid and current
Note: This server provides a convenient interface to AlphaFold data but does not store or cache structure data. All data is retrieved directly from the official AlphaFold API.
If you use this project in your research or publications, please cite it as follows:
author = {Moudather Chelbi},
title = {AlphaFold MCP Server},
year = {2025},
howpublished = {https://github.com/Augmented-Nature/AlphaFold-MCP-Server},
note = {Accessed: 2025-06-29}Analyze confidence score distribution and identify high/low confidence regions
| Name | Required | Description | Default |
|---|---|---|---|
| uniprotId | Yes | UniProt accession |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions analyzing and identifying regions but doesn't describe what the tool actually returns (e.g., statistical summaries, visualizations, or lists), whether it's a read-only operation, performance characteristics, or any side effects. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's function without unnecessary words. It's front-loaded with the core purpose and avoids redundancy. Every part of the sentence contributes essential information, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of analyzing confidence regions and the lack of annotations and output schema, the description is incomplete. It doesn't explain what the analysis entails, what results to expect, or how it differs from related tools. For a tool that likely involves data processing and interpretation, more context is needed to understand its full scope and limitations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the single parameter 'uniprotId' clearly documented as 'UniProt accession'. The description doesn't add any additional meaning beyond what the schema provides, such as explaining how the UniProt ID relates to confidence analysis or what formats are accepted. With high schema coverage, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Analyze confidence score distribution and identify high/low confidence regions'. It specifies the action (analyze and identify) and the resource (confidence score distribution/regions). However, it doesn't explicitly differentiate from sibling tools like 'get_confidence_scores' or 'validate_structure_quality', which might have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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. With sibling tools like 'get_confidence_scores' and 'validate_structure_quality', there's no indication of when this analysis tool is preferred or what specific scenarios it addresses. The description lacks any context about prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Analyze confidence scores for multiple proteins
| Name | Required | Description | Default |
|---|---|---|---|
| uniprotIds | Yes | Array of UniProt accessions (max 30) |
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.
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.
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.
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.
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.
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.
Download multiple structure files
| Name | Required | Description | Default |
|---|---|---|---|
| format | No | File format (default: pdb) | |
| uniprotIds | Yes | Array of UniProt accessions (max 20) |
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 'download' but doesn't disclose behavioral traits like authentication needs, rate limits, file size constraints, or what happens on failure. This is inadequate for a mutation tool with zero annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It is appropriately sized and front-loaded, directly stating the tool's purpose without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and a mutation tool (download implies write operation), the description is incomplete. It lacks critical context like return values, error handling, or operational constraints, making it insufficient for safe agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents parameters (format with enum, uniprotIds with constraints). The description adds no meaning beyond what the schema provides, such as explaining 'structure files' in context of UniProt IDs or format implications.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Download multiple structure files' clearly states the action (download) and resource (structure files), with 'multiple' indicating batch capability. It distinguishes from sibling 'download_structure' by implying batch vs. single operations, though not explicitly named.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives like 'download_structure' or 'get_structure'. The description implies batch downloading but lacks context on prerequisites, limitations, or comparison with siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get structure information for multiple proteins simultaneously
| Name | Required | Description | Default |
|---|---|---|---|
| format | No | Output format (default: json) | |
| uniprotIds | Yes | Array of UniProt accessions (max 50) |
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.
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.
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.
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.
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.
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.
Check if AlphaFold structure prediction is available for a UniProt ID
| Name | Required | Description | Default |
|---|---|---|---|
| uniprotId | Yes | UniProt accession to check |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool checks availability but does not describe what 'available' means (e.g., prediction exists, is accessible, meets quality thresholds), the response format (e.g., boolean, status details), or any limitations (e.g., rate limits, authentication needs). For a query tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that efficiently conveys the core function without unnecessary details. It is front-loaded with the main action ('Check') and resource, making it easy to parse. There is no wasted verbiage, and every word contributes directly to understanding the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
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 a query tool. It does not explain what the tool returns (e.g., availability status, error messages) or behavioral aspects like response time or failure modes. While the purpose is clear, the absence of output and behavioral details makes it inadequate for fully informed use, especially in a context with many sibling tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the single parameter 'uniprotId' documented as 'UniProt accession to check'. The description adds no additional meaning beyond this, such as format examples (e.g., 'P12345') or validation rules. Since the schema fully covers the parameter, the baseline score of 3 is appropriate, as the description does not compensate but also does not detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: checking AlphaFold structure prediction availability for a UniProt ID. It specifies the verb ('check'), resource ('AlphaFold structure prediction'), and target ('UniProt ID'), making the intent unambiguous. However, it does not explicitly differentiate from sibling tools like 'get_structure' or 'download_structure', which might retrieve or download actual structures rather than checking availability.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 does not mention prerequisites, such as needing a valid UniProt ID, or compare it to siblings like 'get_structure' (which might fetch the structure if available) or 'validate_structure_quality' (which might assess existing structures). Without such context, users must infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compare multiple AlphaFold structures for analysis
| Name | Required | Description | Default |
|---|---|---|---|
| uniprotIds | Yes | Array of UniProt accessions to compare (2-10) |
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. It states the tool compares structures 'for analysis', but lacks details on what the comparison entails (e.g., alignment metrics, visual output, or data format), whether it's read-only or has side effects, or any rate limits. This leaves significant gaps in understanding how the tool behaves.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any wasted words. It is appropriately sized and front-loaded, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of comparing protein structures and the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'analysis' involves, the format or content of results, or any behavioral traits, making it inadequate for a tool that likely returns detailed comparison data.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, fully documenting the 'uniprotIds' parameter with its array type and constraints (2-10 items). The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline score for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Compare') and resource ('multiple AlphaFold structures'), providing a specific purpose. However, it doesn't explicitly differentiate from sibling tools like 'find_similar_structures' or 'validate_structure_quality', which might involve comparison aspects, so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description offers no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context, or exclusions, leaving the agent to infer usage from the name alone, which is insufficient for effective tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Download AlphaFold structure file in specified format
| Name | Required | Description | Default |
|---|---|---|---|
| format | No | File format (default: pdb) | |
| uniprotId | Yes | UniProt accession |
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. It states the tool downloads a file but doesn't mention critical details like whether it requires authentication, has rate limits, what happens on errors, or if it's a read-only operation. This leaves significant gaps for an AI agent to understand how to use it safely and effectively.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without any wasted words. It directly communicates what the tool does in a clear and structured manner.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of downloading files (which often involves authentication, error handling, and output details) and the lack of annotations and output schema, the description is incomplete. It doesn't address what the downloaded file contains, how it's delivered, or any prerequisites, making it inadequate for safe and informed use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 (uniprotId and format with enum values). The description adds no additional meaning beyond what's in the schema, such as explaining what an AlphaFold structure is or why these parameters are needed, resulting in the baseline score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Download') and resource ('AlphaFold structure file'), making the purpose immediately understandable. However, it doesn't differentiate this tool from sibling tools like 'batch_download' or 'get_structure', which appear to offer similar functionality, preventing a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 'batch_download' or 'get_structure' from the sibling list. It mentions a format specification but doesn't explain why one might choose this tool over others for downloading structures.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Export structure data formatted for ChimeraX visualization
| Name | Required | Description | Default |
|---|---|---|---|
| includeConfidence | No | Include confidence score coloring (default: true) | |
| uniprotId | Yes | UniProt accession |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers minimal behavioral insight. It states the tool exports formatted data but doesn't disclose what format is used, whether it's a file or data stream, if authentication is needed, rate limits, or what happens on failure. 'Export' implies a read operation, but this isn't explicitly confirmed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized for a simple export tool and front-loads the key information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations, no output schema, and 2 parameters, the description is incomplete. It doesn't explain what the exported data looks like, how it's delivered, or any behavioral constraints. While concise, it lacks necessary context for an agent to understand the full implications of using this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents both parameters. The description adds no parameter-specific information beyond what's in the schema (e.g., it doesn't explain how 'includeConfidence' affects ChimeraX visualization or provide examples for 'uniprotId'). Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'export' and resource 'structure data' with the specific purpose 'for ChimeraX visualization'. It distinguishes from sibling 'export_for_pymol' by specifying a different visualization tool, though it doesn't explicitly contrast with other export/download tools like 'download_structure' or 'batch_download'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 when to choose this over 'export_for_pymol' for different visualization needs, or when to use it instead of 'download_structure' for raw data. No prerequisites, exclusions, or comparative context are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Export structure data formatted for PyMOL visualization
| Name | Required | Description | Default |
|---|---|---|---|
| includeConfidence | No | Include confidence score coloring (default: true) | |
| uniprotId | Yes | UniProt accession |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool exports formatted data but doesn't describe what format is produced (e.g., PDB file, script), whether it's a file download or data return, authentication requirements, rate limits, or potential side effects. This leaves significant behavioral gaps for a tool that presumably generates output.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that communicates the core purpose without unnecessary words. It's appropriately sized for a straightforward export tool and front-loads the essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For an export tool with no annotations and no output schema, the description is insufficient. It doesn't explain what format the output takes, whether it's downloadable data or a visualization script, or what the agent should expect as a result. Given the complexity of structure data export and lack of structured metadata, more behavioral context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds no additional parameter information beyond what the schema provides - it doesn't explain how 'includeConfidence' affects PyMOL visualization or provide context about UniProt IDs. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Export') and resource ('structure data') with specific formatting purpose ('formatted for PyMOL visualization'). It distinguishes from obvious sibling 'export_for_chimerax' by specifying the target visualization tool, though doesn't explicitly differentiate from other export/download tools like 'download_structure' or 'batch_download'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives is provided. The description implies usage for PyMOL visualization needs, but doesn't specify when to choose this over 'export_for_chimerax' or other structure retrieval tools, nor does it mention prerequisites or constraints beyond what's in the schema.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find AlphaFold structures similar to a given protein
| Name | Required | Description | Default |
|---|---|---|---|
| organism | No | Filter by organism (optional) | |
| uniprotId | Yes | Reference UniProt accession |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool finds similar structures but does not explain what 'similar' means (e.g., by sequence, structure, or other metrics), how results are returned (e.g., list, scores), or any limitations like rate limits or authentication needs. This leaves significant gaps in understanding the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that efficiently conveys the core purpose without unnecessary words. It is front-loaded and wastes no space, making it highly concise and well-structured for quick comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of finding similar protein structures, the lack of annotations, and no output schema, the description is insufficient. It does not cover what 'similar' entails, the format or scope of results, or any behavioral traits like performance or constraints, making it incomplete for effective tool use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters ('organism' and 'uniprotId') adequately. The description does not add any meaning beyond the schema, such as explaining the role of 'uniprotId' as the reference for similarity or providing examples. Thus, it meets the baseline but does not enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Find') and resource ('AlphaFold structures similar to a given protein'), making it easy to understand what the tool does. However, it does not explicitly differentiate from sibling tools like 'search_structures' or 'compare_structures', which might have overlapping functionality, so it falls short of a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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. With sibling tools like 'search_structures' and 'compare_structures' available, there is no indication of scenarios where this tool is preferred, prerequisites, or exclusions, leaving usage ambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check AlphaFold API status and database statistics
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool checks status and statistics, implying a read-only operation, but doesn't specify aspects like rate limits, authentication needs, response format, or whether it's a real-time check versus cached data. This leaves significant gaps for an agent to understand the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any unnecessary words. It's front-loaded with the core action and resources, making it easy to parse and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is adequate as a basic overview. However, for a status-checking tool in a complex API context with many siblings, it lacks details on what specific statistics are returned or how it differs from similar tools, leaving room for improvement in guiding an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and the schema description coverage is 100%, so there are no parameters to document. The description doesn't need to add parameter semantics, and it appropriately doesn't mention any. This meets the baseline for a parameterless tool, though it doesn't go beyond that.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Check') and resources ('AlphaFold API status and database statistics'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'check_availability' or 'get_organism_stats', which might have overlapping or related functionality in the same domain.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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. With sibling tools like 'check_availability' and 'get_organism_stats' that might relate to status or statistics, there's no indication of context, prerequisites, or exclusions to help an agent choose appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get per-residue confidence scores for a structure prediction
| Name | Required | Description | Default |
|---|---|---|---|
| threshold | No | Confidence threshold (0-100, optional) | |
| uniprotId | Yes | UniProt accession |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does but does not cover critical aspects like whether it's a read-only operation, potential rate limits, authentication needs, or what the output format looks like (e.g., list of scores, JSON structure). This leaves significant gaps for agent understanding.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and efficiently communicates the core function, 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.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of structure prediction tools and the lack of annotations and output schema, the description is insufficient. It does not explain return values (e.g., format of confidence scores), error conditions, or behavioral traits, leaving the agent with incomplete information for proper tool invocation and result interpretation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, clearly documenting both parameters (uniprotId and threshold). The description does not add any additional meaning beyond the schema, such as explaining residue-level details or threshold implications. With high schema coverage, the baseline score of 3 is appropriate as the schema handles the parameter documentation adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get') and target ('per-residue confidence scores for a structure prediction'), making the purpose understandable. However, it does not explicitly differentiate from sibling tools like 'analyze_confidence_regions' or 'validate_structure_quality', which might also involve confidence metrics, so it lacks sibling distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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. With siblings such as 'analyze_confidence_regions' and 'validate_structure_quality' that may relate to confidence, there is no indication of specific use cases, exclusions, or prerequisites, leaving the agent without contextual direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get information about sequence coverage in the AlphaFold prediction
| Name | Required | Description | Default |
|---|---|---|---|
| uniprotId | Yes | UniProt accession |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. It states what the tool does but doesn't describe output format, error conditions, rate limits, or whether it's a read-only operation. For a tool with no annotations, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused sentence with no wasted words. It's appropriately sized for a simple tool with one parameter and gets straight to the point without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
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 insufficiently complete. It doesn't explain what 'sequence coverage information' includes, the format of returned data, or how this differs from other information-retrieval tools in the sibling set. The agent would need to guess about the tool's behavior and output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the single parameter 'uniprotId' documented as 'UniProt accession'. The description doesn't add any parameter-specific information beyond what the schema provides, so it 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get information') and resource ('sequence coverage in the AlphaFold prediction'), providing a specific purpose. However, it doesn't explicitly differentiate from sibling tools like 'get_confidence_scores' or 'get_prediction_metadata' that also retrieve AlphaFold-related information, which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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. The description doesn't mention prerequisites, context, or exclusions, leaving the agent to infer usage from the tool name alone among many similar-sounding siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get statistics about AlphaFold coverage for an organism
| Name | Required | Description | Default |
|---|---|---|---|
| organism | Yes | Organism name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'Get statistics' but doesn't specify what kind of statistics, whether it's a read-only operation, if there are rate limits, or how results are returned. This leaves significant gaps for an agent to understand the tool's behavior beyond basic purpose.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any unnecessary words. It's front-loaded and appropriately sized for its function, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
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 insufficiently complete. It doesn't explain what statistics are returned, how they're formatted, or any behavioral traits like error handling. For a tool with no structured metadata, more detail is needed to fully guide an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'organism' parameter clearly documented as 'Organism name'. The description doesn't add any extra meaning beyond this, such as format examples or constraints, so it meets the baseline for adequate but minimal parameter information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get statistics') and resource ('AlphaFold coverage for an organism'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_coverage_info' or 'list_by_organism', which might have overlapping functionality, so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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. With siblings like 'get_coverage_info' and 'list_by_organism' that might relate to organism data, there's no indication of specific contexts, exclusions, or comparisons to help an agent choose appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get metadata about the prediction including version, date, and quality metrics
| Name | Required | Description | Default |
|---|---|---|---|
| uniprotId | Yes | UniProt accession |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves metadata but doesn't describe how it behaves: e.g., whether it's a read-only operation, if it requires authentication, potential rate limits, error handling, or the format of returned data. For a tool with no annotation coverage, this leaves significant gaps in understanding its operational traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('Get metadata about the prediction') and specifies key details (version, date, quality metrics). There is no wasted verbiage, repetition, or unnecessary elaboration, making it appropriately sized for its informational content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (a metadata retrieval tool with one parameter), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what a 'prediction' refers to in this context (e.g., protein structure prediction), the source or scope of the metadata, or what the output looks like. For a tool in a scientific domain with siblings like 'analyze_confidence_regions', more context is needed to ensure proper use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the parameter 'uniprotId' documented as 'UniProt accession'. The description doesn't add any meaning beyond this, such as explaining what a UniProt ID is, format examples, or how it relates to the metadata. With high schema coverage, the baseline is 3, as the description doesn't compensate but doesn't detract either.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get metadata') and the resource ('prediction'), specifying what information is retrieved (version, date, quality metrics). It distinguishes from siblings like 'get_confidence_scores' or 'get_structure' by focusing on metadata rather than scores or structure files. However, it doesn't explicitly mention the domain (e.g., protein structure prediction) or differentiate from 'get_api_status' which might also provide metadata.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 specify prerequisites (e.g., needing a UniProt ID), compare to siblings like 'check_availability' or 'get_coverage_info', or indicate scenarios where metadata is needed over other data types. Usage is implied only by the tool name and description, with no explicit context or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get AlphaFold structure prediction for a specific UniProt ID
| Name | Required | Description | Default |
|---|---|---|---|
| format | No | Output format (default: json) | |
| uniprotId | Yes | UniProt accession (e.g., P21359, Q8N726) |
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 without mentioning permissions, rate limits, response format, or error handling. It fails to add meaningful context beyond the basic operation, leaving key behavioral traits unspecified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste—it directly states the tool's purpose without unnecessary words or structural fluff, making it easy to parse and front-loaded with essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of retrieving structure predictions, lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the output contains (e.g., coordinates, confidence scores), potential errors, or dependencies, leaving significant gaps for effective tool use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents both parameters (uniprotId and format). The description adds no additional parameter semantics beyond what the schema provides, such as explaining format implications or uniprotId validation, meeting the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get'), resource ('AlphaFold structure prediction'), and target ('for a specific UniProt ID'), distinguishing it from siblings like 'download_structure' or 'get_confidence_scores' by focusing on retrieval of the primary prediction data rather than downloading files or obtaining metadata.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 'download_structure' (which might handle file downloads) or 'get_prediction_metadata' (for metadata). It lacks explicit when/when-not instructions or named alternatives, leaving usage context implied at best.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all available structures for a specific organism
| Name | Required | Description | Default |
|---|---|---|---|
| organism | Yes | Organism name (e.g., "Homo sapiens", "Escherichia coli") | |
| size | No | Number of results (1-100, default: 50) |
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. It states it 'lists all available structures' but doesn't clarify what 'available' means (e.g., public vs. restricted), whether it's a read-only operation, if there are rate limits, or the format of the output. 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('List all available structures for a specific organism'). There is no wasted text, making it highly concise and well-structured for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
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. It doesn't explain what 'structures' entail (e.g., protein models, datasets), the return format, or any behavioral traits like pagination or errors. For a tool in a scientific context with many siblings, more detail is needed to ensure proper use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters ('organism' and 'size') thoroughly. The description adds no additional meaning beyond implying organism filtering, which is already clear 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'list' and the resource 'structures', specifying it's for a 'specific organism'. It distinguishes from siblings like 'search_structures' or 'get_structure' by focusing on organism-based listing. However, it doesn't explicitly differentiate from 'get_organism_stats', which might overlap in scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 'search_structures' or 'get_organism_stats'. It lacks context on prerequisites, exclusions, or comparative use cases, leaving the agent to 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.
Search for available AlphaFold structures by protein name or gene
| Name | Required | Description | Default |
|---|---|---|---|
| organism | No | Filter by organism (optional) | |
| query | Yes | Search term (protein name, gene name, etc.) | |
| size | No | Number of results (1-100, default: 25) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions 'search' and 'available AlphaFold structures', it doesn't describe what 'available' means, whether results are paginated, what format they return, or any rate limits or authentication requirements. For a search tool with no annotation coverage, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that clearly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded with the essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a search tool with no annotations and no output schema, the description is insufficient. It doesn't explain what the search returns (e.g., structure IDs, metadata), how results are ordered, or any limitations. With 3 parameters and many sibling tools, more context is needed for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all three parameters thoroughly. The description adds minimal value beyond the schema by mentioning 'protein name or gene' as search terms, which aligns with the query parameter description. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search for available AlphaFold structures by protein name or gene'. It specifies the verb ('Search'), resource ('AlphaFold structures'), and search criteria ('protein name or gene'). However, it doesn't explicitly differentiate from sibling tools like 'find_similar_structures' or 'list_by_organism', which also involve structure retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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. With many sibling tools for structure retrieval (e.g., 'find_similar_structures', 'list_by_organism', 'get_structure'), there's no indication of when this search-based approach is preferred or what its limitations might be.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Validate and assess the overall quality of an AlphaFold prediction
| Name | Required | Description | Default |
|---|---|---|---|
| uniprotId | Yes | UniProt accession |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool validates and assesses quality, implying a read-only analysis, but doesn't describe what 'validate' entails (e.g., checks for errors, compares to standards), what 'assess' outputs (e.g., scores, reports), or any constraints like rate limits or authentication needs. This leaves significant gaps for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any fluff or redundancy. It is appropriately sized and front-loaded, with every word contributing to understanding the tool's function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of validating and assessing prediction quality, the lack of annotations and output schema, and the presence of many sibling tools, the description is incomplete. It doesn't explain what 'quality' means in this context, what the output includes (e.g., scores, validation reports), or how it differs from related tools, leaving the agent with insufficient information to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the single parameter 'uniprotId' documented as 'UniProt accession'. The description adds no additional meaning beyond this, such as explaining how the UniProt ID maps to the AlphaFold prediction or any format specifics. With high schema coverage, the baseline score of 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('validate and assess') and the target resource ('overall quality of an AlphaFold prediction'), providing a specific verb+resource combination. However, it doesn't distinguish this tool from its many siblings (like 'analyze_confidence_regions' or 'get_confidence_scores'), which might also assess prediction aspects, so it doesn't reach the highest clarity level.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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. With 18 sibling tools, including ones like 'get_confidence_scores' or 'compare_structures' that might overlap in assessing prediction quality, the lack of explicit when/when-not usage or named alternatives leaves the agent without clear direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.0.0analyze_confidence_regionsbatch_confidence_analysisbatch_downloadbatch_structure_infocheck_availabilitycompare_structuresdownload_structureexport_for_chimeraxexport_for_pymolfind_similar_structuresget_api_statusget_confidence_scoresget_coverage_infoget_organism_statsget_prediction_metadataget_structurelist_by_organismsearch_structuresvalidate_structure_qualityMost tools have distinct purposes, but some overlap exists: 'get_structure' and 'download_structure' could be confused, as both retrieve structure files, though 'download_structure' specifies format. Similarly, 'batch_structure_info' and 'get_prediction_metadata' both provide metadata, but descriptions clarify different focuses. Overall, descriptions help mitigate ambiguity, but a few tools have unclear boundaries.
Tool names follow a highly consistent verb_noun pattern throughout, with clear, descriptive actions (e.g., 'analyze_confidence_regions', 'batch_download', 'check_availability'). All names use snake_case uniformly, and verbs are well-chosen to indicate distinct operations, making the set predictable and readable.
With 19 tools, the count is slightly high but reasonable for the AlphaFold domain, which involves complex workflows like structure retrieval, analysis, visualization, and batch processing. Each tool appears to earn its place by covering specific aspects, though it might feel heavy for simpler use cases.
The tool surface provides comprehensive coverage for AlphaFold structure prediction workflows, including availability checks, retrieval, metadata, confidence analysis, visualization exports, batch operations, and search functions. There are no obvious gaps; agents can perform full CRUD-like operations and advanced analyses without dead ends.
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AlphaFold DB MCP — predicted protein structures.
UniProt MCP — protein sequence + function database.
Protein analysis: ESM-2/ESMC embeddings, mutation scoring, landscape scans, ESMFold structure.
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