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Augmented-Nature

PDB MCP Server

by Augmented-Nature
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PDB MCP Server Logo

PDB MCP Server

A Model Context Protocol (MCP) server that provides access to the Protein Data Bank (PDB) - the worldwide repository of information about the 3D structures of proteins, nucleic acids, and complex assemblies.

Features

Tools

  • search_structures: Search PDB database for protein structures by keyword, protein name, or PDB ID

  • get_structure_info: Get detailed information for a specific PDB structure

  • download_structure: Download structure coordinates in various formats (PDB, mmCIF, mmTF, XML)

  • search_by_uniprot: Find PDB structures associated with a UniProt accession

  • get_structure_quality: Get structure quality metrics and validation data

Resources

  • pdb://structure/{pdb_id}: Complete structure information for a PDB ID

  • pdb://coordinates/{pdb_id}: Structure coordinates in PDB format

  • pdb://mmcif/{pdb_id}: Structure data in mmCIF format

  • pdb://validation/{pdb_id}: Structure validation data and quality metrics

  • pdb://ligands/{pdb_id}: Ligand and binding site information

  • pdb://search/{query}: Search results for structures matching the query

Related MCP server: AlphaFold MCP Server

Installation

npm install
npm run build

Usage

With Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "pdb-server": {
      "command": "node",
      "args": ["/path/to/pdb-server/build/index.js"]
    }
  }
}

Example Queries

  1. Search for insulin structures:

    Use the search_structures tool to find insulin protein structures

  2. Get information about a specific structure:

    Get detailed information about PDB structure 1ABC using get_structure_info

  3. Download structure coordinates:

    Download the PDB file for structure 1ABC using download_structure

  4. Find structures for a UniProt protein:

    Find PDB structures for UniProt accession P01308 using search_by_uniprot

  5. Check structure quality:

    Get validation metrics for PDB structure 1ABC using get_structure_quality

  6. Access resources directly:

    Show me the resource pdb://structure/1abc

API Integration

This server integrates with:

Structure Data Formats

  • PDB: Original Protein Data Bank format

  • mmCIF: Macromolecular Crystallographic Information File (newer standard)

  • mmTF: Macromolecular Transmission Format (binary, compressed)

  • XML: Extensible Markup Language format

Quality Metrics

The server provides various structure quality indicators:

  • Resolution (for X-ray crystallography)

  • R-work and R-free values

  • Ramachandran plot statistics

  • Clash scores

  • Rotamer outliers

  • Model validation scores

Complementary Servers

This PDB server works well with:

  • UniProt MCP Server: For protein sequence and functional information

  • AlphaFold MCP Server: For predicted protein structures

Together, these provide comprehensive access to protein structure and function data.

Error Handling

The server includes robust error handling for:

  • Invalid PDB IDs

  • Network connectivity issues

  • API rate limiting

  • Missing structure files

  • Malformed requests

Development

# Install dependencies
npm install

# Build the server
npm run build

# Run in development mode
npm run dev

Attribution

This project is developed by Augmented Nature 🌐 Website: augmentednature.ai

Citation

If you use this project in your research or publications, please cite it as follows:

author = {Moudather Chelbi},
title = {Protein Data Bank (PDB) MCP Server},
year = {2025},
howpublished = {https://github.com/Augmented-Nature/PDB-MCP-Server},
note = {Accessed: 2025-06-29}

Available Tools

5 tools
download_structureC

Download structure coordinates in various formats

ParametersJSON Schema
NameRequiredDescriptionDefault
pdb_idYesPDB ID (4-character code)
formatNoFile format (default: pdb)
assembly_idNoBiological assembly ID (optional)

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations, the description carries full burden but lacks behavioral details. It doesn't disclose whether this is a read-only operation (implied by 'download'), authentication requirements, rate limits, file size considerations, or what happens on failure (e.g., invalid PDB ID). The phrase 'various formats' is vague without context on format differences.

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 a single, efficient sentence that front-loads the core purpose. It avoids redundancy but could be slightly more informative (e.g., specifying typical use cases). Every word earns its place, though it's borderline minimal.

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 3 parameters with full schema coverage and no output schema, the description is adequate but incomplete. It covers the basic purpose but lacks context on behavioral traits (e.g., network effects, error handling) and usage guidelines, which are important for a download operation with format options and no annotations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents parameters. The description adds no additional meaning beyond implying format flexibility ('various formats'), which is already covered by the enum in 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 action ('Download') and resource ('structure coordinates'), specifying the resource type and output format variability. It distinguishes from siblings like 'get_structure_info' (metadata retrieval) and 'get_structure_quality' (quality metrics) by focusing on file download, though it doesn't explicitly name these alternatives.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. The description mentions 'various formats' but doesn't specify which format to choose for different use cases or compare to sibling tools like 'search_structures' for finding structures before downloading.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_structure_infoC

Get detailed information for a specific PDB structure

ParametersJSON Schema
NameRequiredDescriptionDefault
pdb_idYesPDB ID (4-character code, e.g., 1ABC)
formatNoOutput format (default: json)

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. It states the action but lacks details on permissions, rate limits, error handling, or what 'detailed information' entails (e.g., metadata, coordinates, annotations). This is a significant gap for a tool with potential data retrieval complexities.

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, direct sentence with zero waste—it states the purpose without unnecessary words. It's appropriately sized and front-loaded, 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 the lack of annotations and output schema, the description is incomplete. It doesn't clarify what 'detailed information' includes (e.g., JSON structure, file content), potential behavioral traits like authentication needs, or how it differs from siblings. For a data retrieval tool with undefined outputs, this leaves critical gaps for an 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?

Schema description coverage is 100%, so the input schema already documents both parameters thoroughly (PDB ID format and output format options). The description adds no additional parameter semantics beyond implying the tool fetches data for a given PDB ID, which is redundant with 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 resource ('detailed information for a specific PDB structure'), making the purpose evident. However, it doesn't explicitly differentiate from sibling tools like 'get_structure_quality' or 'download_structure', which might also retrieve structure-related data but with different scopes or formats.

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. For example, it doesn't specify if this is for metadata retrieval versus downloading raw files (compared to 'download_structure') or quality metrics (compared to 'get_structure_quality'), leaving the agent to infer usage from tool names alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_structure_qualityC

Get structure quality metrics and validation data

ParametersJSON Schema
NameRequiredDescriptionDefault
pdb_idYesPDB ID (4-character code)

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. It states what the tool does but doesn't add any context about traits like whether it's read-only, has rate limits, requires authentication, or what the output format might be. For a tool with zero annotation coverage, this is a significant gap in transparency.

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 extremely concise and front-loaded, consisting of a single, clear sentence that directly states the tool's purpose without any wasted words. Every part of the description earns its place by conveying essential information efficiently.

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 complexity of retrieving quality metrics and validation data, the description is incomplete. With no annotations and no output schema, it doesn't explain what 'quality metrics and validation data' entail, such as specific metrics returned or the response format. This leaves the agent with insufficient context for effective use.

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 input schema has 100% description coverage, with the single parameter 'pdb_id' clearly documented as a 'PDB ID (4-character code)'. The description doesn't add any extra meaning beyond this, such as examples or constraints, so it meets the baseline score of 3 where 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 tool's purpose with a specific verb ('Get') and resource ('structure quality metrics and validation data'), making it immediately understandable. However, it doesn't explicitly differentiate this tool from its siblings like 'get_structure_info' or 'search_structures', which might also provide structural information, 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.

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 any context, prerequisites, or exclusions, such as how it differs from 'get_structure_info' or when to prefer 'search_by_uniprot'. This lack of usage direction leaves the agent without clear decision-making criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

search_by_uniprotC

Find PDB structures associated with a UniProt accession

ParametersJSON Schema
NameRequiredDescriptionDefault
uniprot_idYesUniProt accession number
limitNoNumber of results to return (1-1000, default: 25)

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It states the tool finds structures but doesn't disclose behavioral traits like whether it's read-only, if it has rate limits, authentication needs, or what the output format looks like. For a search tool with no annotation coverage, this leaves significant gaps in understanding how it behaves.

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 that directly states the tool's purpose without any fluff. It's appropriately sized and front-loaded, making it easy to understand at a glance. Every word earns its place, achieving optimal conciseness.

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 tool has no annotations and no output schema, the description is incomplete. It doesn't explain what the return values are (e.g., list of PDB IDs, metadata), behavioral aspects, or error handling. For a search tool with 2 parameters, this minimal description leaves too much unspecified for effective use.

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 input schema has 100% description coverage, clearly documenting both parameters ('uniprot_id' and 'limit') with details like default values and constraints. The description adds no additional meaning beyond what the schema provides, such as examples or edge cases. With high schema coverage, the baseline is 3, 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 tool's purpose: 'Find PDB structures associated with a UniProt accession.' It specifies the action ('Find') and resource ('PDB structures'), and distinguishes from siblings like 'download_structure' or 'get_structure_info' by focusing on searching by UniProt ID. However, it doesn't explicitly differentiate from 'search_structures', which might be a broader search tool, so it's not a perfect 5.

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 when to choose it over 'search_structures' or other siblings, nor does it specify prerequisites or exclusions. The usage is implied by the purpose but lacks explicit context, making it minimal guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

search_structuresC

Search PDB database for protein structures by keyword, protein name, or PDB ID

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYesSearch query (protein name, keyword, PDB ID, etc.)
limitNoNumber of results to return (1-1000, default: 25)
sort_byNoSort results by (release_date, resolution, etc.)
experimental_methodNoFilter by experimental method (X-RAY, NMR, ELECTRON MICROSCOPY)
resolution_rangeNoResolution range filter (e.g., "1.0-2.0")

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. It mentions searching but doesn't cover critical aspects like whether this is a read-only operation, potential rate limits, authentication needs, or what the response format looks like (e.g., list of structures with metadata). For a search tool with 5 parameters, 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.

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 without unnecessary words. Every part ('search PDB database', 'protein structures', 'by keyword, protein name, or PDB ID') contributes directly to understanding the tool's function.

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 complexity (5 parameters, no annotations, no output schema), the description is incomplete. It lacks information about behavioral traits, output format, and usage context relative to siblings. While concise, it doesn't provide enough detail for an agent to fully understand how to invoke and interpret results from this tool.

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 implies the 'query' parameter's purpose but doesn't add meaningful semantics beyond what the schema already provides. With 100% schema description coverage, the baseline is 3, as the schema adequately documents all parameters (e.g., 'limit' range, 'experimental_method' values). No extra parameter insights are offered.

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 ('search'), resource ('PDB database for protein structures'), and search methods ('by keyword, protein name, or PDB ID'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'search_by_uniprot' or 'get_structure_info', 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.

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 'search_by_uniprot' or 'get_structure_info', nor does it mention prerequisites or exclusions. It simply states what the tool does without contextual usage information.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 5 tool updates
    • First observeddownload_structure
    • First observedget_structure_info
    • First observedget_structure_quality
    • First observedsearch_by_uniprot
    • First observedsearch_structures
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TDQS

A3.5/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: downloading structure data, retrieving structure metadata, accessing quality metrics, searching by UniProt ID, and general keyword/ID search. There is no overlap in functionality, making tool selection straightforward for an agent.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern with snake_case (e.g., download_structure, get_structure_info, search_by_uniprot). The naming is uniform and predictable across all five tools.

Tool Count5/5

Five tools is well-scoped for a PDB (Protein Data Bank) server, covering core operations like data retrieval, search, and quality assessment without being too sparse or overwhelming. Each tool serves a clear, necessary function.

Completeness4/5

The toolset provides strong coverage for querying and retrieving PDB structure data, including search, info, quality, and download functions. A minor gap might be the lack of tools for advanced filtering or batch operations, but core workflows are well-supported.

Maintenance

ActivityInactive
ResponsivenessSyncing

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