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., "@MCP Materials Server search for lithium cobalt oxide materials"
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 Model Context Protocol (MCP) server that provides AI assistants with access to materials science databases, starting with the Materials Project API.
Built with the MCP Python SDK using FastMCP.
Tool | Description |
| Search materials by chemical formula (e.g., "Fe2O3", "LiFePO4") |
| Retrieve crystal structure in CIF, POSCAR, or JSON format |
| Get comprehensive material properties (band gap, formation energy, etc.) |
| Side-by-side comparison of multiple materials |
| Find materials containing/excluding specific elements |
| Search by electronic band gap range (eV) |
| Find materials with similar crystal structures |
| Phase stability analysis for chemical systems |
| Mechanical properties (bulk/shear modulus, Debye temperature) |
| Find materials by mechanical property ranges |
Resource | URI | Description |
Periodic Table |
| Element data with atomic numbers and masses |
Crystal Systems |
| The 7 crystal systems with symmetry constraints |
Prompt | Description |
| Comprehensive analysis workflow for a material ID |
| Search for battery electrode candidates |
| Compare phases in an alloy system |
Related MCP server: Materials Project Platform MCP Server
Python 3.11 or higher
Materials Project API key (get one free)
# Clone or navigate to the project
cd mcp-materials-server
# Create virtual environment
python -m venv .venv
# Activate virtual environment
# On Windows:
.venv\Scripts\activate
# On macOS/Linux:
source .venv/bin/activate
# Install the package
pip install -e ".[dev]"# On Windows (PowerShell):
$env:MP_API_KEY = "your_api_key_here"
# On Windows (CMD):
set MP_API_KEY=your_api_key_here
# On macOS/Linux:
export MP_API_KEY="your_api_key_here"# Using the installed command
mcp-materials
# Or run directly
python -m mcp_materials.serverAdd to your Claude Desktop configuration file:
Location:
Windows: %APPDATA%\Claude\claude_desktop_config.json
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Configuration:
{
"mcpServers": {
"materials": {
"command": "python",
"args": ["-m", "mcp_materials.server"],
"cwd": "D:\\path\\to\\mcp-materials-server",
"env": {
"MP_API_KEY": "your_api_key_here"
}
}
}
}After adding the configuration, restart Claude Desktop.
Once connected to Claude, you can ask:
"Search for lithium cobalt oxide materials"
"Find materials with formula Fe2O3"
"Search for materials containing Li, Fe, and O"
"Get the properties of mp-149 (Silicon)"
"What is the band gap of mp-19017?"
"Get the crystal structure of mp-149 in CIF format"
"Find materials with band gap between 1.5 and 2.5 eV"
"Get the elastic properties of silicon (mp-149)"
"Generate a phase diagram for the Li-Fe-O system"
"Compare the properties of LiCoO2 and LiFePO4"
"Find stiff materials with bulk modulus > 200 GPa"
"Analyze material mp-149 comprehensively"
"Find potential Li-ion battery cathode materials"
"Compare phases in the Fe-Cr-Ni alloy system"
# Run all tests
pytest
# Run with verbose output
pytest -v
# Run specific test class
pytest tests/test_server.py::TestToolFunctions -v# Check for issues
ruff check src/
# Auto-format
ruff format src/mcp-materials-server/
├── src/
│ └── mcp_materials/
│ ├── __init__.py # Package version
│ └── server.py # MCP server (10 tools, 2 resources, 3 prompts)
├── tests/
│ ├── __init__.py
│ ├── conftest.py # Pytest configuration
│ └── test_server.py # Comprehensive test suite
├── pyproject.toml # Project configuration
├── claude_desktop_config.example.json
├── .gitignore
└── README.mdsearch_materials(formula, max_results=10)Search by chemical formula. Returns material IDs, band gaps, formation energies, and stability.
get_structure(material_id, format="cif")Get crystal structure. Formats: cif, poscar, json.
get_properties(material_id)Full property set: composition, symmetry, electronic, thermodynamic properties.
compare_materials(material_ids)Compare list of materials side-by-side.
search_by_elements(elements, exclude_elements=None, max_results=10)Find materials by element composition.
search_by_band_gap(min_gap=0, max_gap=10, direct_gap_only=False, max_results=10)Search by band gap range in eV.
get_similar_structures(material_id, max_results=5)Find materials with same space group.
get_phase_diagram(elements)Build phase diagram for chemical system. Returns stable/unstable phases with decomposition products.
get_elastic_properties(material_id)Mechanical properties: bulk modulus, shear modulus (Voigt/Reuss/VRH), Poisson ratio, Debye temperature.
search_by_elastic_properties(min_bulk_modulus=None, max_bulk_modulus=None, min_shear_modulus=None, max_shear_modulus=None, max_results=10)Filter materials by mechanical properties.
Add AFLOW database integration
Add OQMD database support
Add electronic structure (DOS, band structure) tools
Add XRD pattern simulation
Add synthesis route suggestions
Add surface/interface properties
MIT
Hesham Salama
Compare properties of multiple materials side by side.
Args:
material_ids: List of Materials Project IDs (e.g., ["mp-149", "mp-66"])
Returns:
JSON table comparing key properties across materials
| Name | Required | Description | Default |
|---|---|---|---|
| material_ids | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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. While it states what the tool does (comparison) and the return format (JSON table), it lacks important behavioral context such as whether this is a read-only operation, what authentication might be required, rate limits, what specific 'key properties' are included, or how many materials can be compared at once.
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 perfectly structured and concise. It begins with a clear purpose statement, then provides an 'Args:' section with parameter explanation, followed by a 'Returns:' section. Every sentence earns its place, and the information is front-loaded with the most important purpose statement first.
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 that there's an output schema (though not provided in the context), the description doesn't need to fully explain return values. The description covers the essential purpose, parameter semantics, and return format. However, for a comparison tool with no annotations, it could benefit from more behavioral context about what 'key properties' means or limitations of the comparison.
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 significant value beyond the input schema, which has 0% description coverage. It explains that 'material_ids' should be 'List of Materials Project IDs' and provides concrete examples (["mp-149", "mp-66"]). This transforms a generic array parameter into a specific, meaningful input requirement that the schema alone doesn't convey.
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: 'Compare properties of multiple materials side by side.' This specifies the verb (compare) and resource (materials properties) with the scope of side-by-side comparison. However, it doesn't explicitly differentiate from sibling tools like 'get_properties' or 'search_by_band_gap' that might also provide material property information.
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. There are multiple sibling tools that deal with material properties (get_properties, search_by_band_gap, search_by_elastic_properties, etc.), but the description offers no context about when this comparison tool is preferable to those individual property retrieval tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get elastic and mechanical properties for a material.
Args:
material_id: Materials Project ID (e.g., "mp-149" for Silicon)
Returns:
JSON with elastic properties including bulk modulus, shear modulus,
Young's modulus, Poisson's ratio, and elastic tensor
| Name | Required | Description | Default |
|---|---|---|---|
| material_id | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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 describes a read operation ('Get') and specifies the return format, but doesn't cover important aspects like error handling, rate limits, authentication needs, or whether the data is cached or real-time. For a tool with no annotations, 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 well-structured and concise, with a clear purpose statement followed by 'Args:' and 'Returns:' sections. Every sentence adds value, and it's front-loaded with the main functionality. There's no wasted text, making it efficient for an agent to parse.
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 has an output schema (as per context signals), the description doesn't need to detail return values, and it adequately covers the input parameter. However, with no annotations and a read operation that might involve complex data retrieval, it could benefit from more behavioral context (e.g., data freshness, limitations). Overall, it's mostly complete but has minor gaps.
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 meaningful context for the single parameter 'material_id' by explaining it's a 'Materials Project ID' and providing an example ('e.g., "mp-149" for Silicon'). Since schema description coverage is 0% and there's only one parameter, this compensates well, giving the agent clear guidance on what to input.
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: 'Get elastic and mechanical properties for a material.' It specifies the verb ('Get'), resource ('elastic and mechanical properties'), and target ('a material'). However, it doesn't explicitly differentiate from sibling tools like 'get_properties' or 'search_by_elastic_properties', which might have overlapping functionality, so it misses 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. It doesn't mention sibling tools, prerequisites, or specific contexts for usage. The only implied usage is for retrieving elastic properties, but without explicit comparisons or exclusions, it lacks sufficient guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get phase diagram data for a chemical system.
Args:
elements: List of elements defining the system (e.g., ["Li", "Fe", "O"] for Li-Fe-O system)
Returns:
JSON with phase diagram entries including stable phases, formation energies, and decomposition products
| Name | Required | Description | Default |
|---|---|---|---|
| elements | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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 'Get phase diagram data' but doesn't describe if this is a read-only operation, requires authentication, has rate limits, or what happens on errors. For a tool with zero annotation coverage, this is a significant gap in behavioral context.
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 appropriately sized and well-structured, with clear sections for Args and Returns. Each sentence adds value without redundancy, 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 complexity (chemical system analysis), the description is reasonably complete: it explains the purpose, parameter semantics, and return values. With an output schema present, it doesn't need to detail return values further, but could improve by adding usage guidelines and behavioral transparency.
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 meaningful context beyond the input schema, which has 0% description coverage. It explains that 'elements' is a 'List of elements defining the system' and provides an example (['Li', 'Fe', 'O'] for Li-Fe-O system), clarifying the parameter's purpose and format effectively.
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 ('Get') and resource ('phase diagram data for a chemical system'), making it easy to understand what it does. However, it doesn't explicitly differentiate from sibling tools like 'search_by_elements' or 'get_properties', which might also involve chemical systems, so it misses full 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. It doesn't mention prerequisites, exclusions, or compare to sibling tools like 'search_by_elements' or 'get_properties', leaving the agent without context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get detailed properties for a specific material from Materials Project.
Args:
material_id: Materials Project ID (e.g., "mp-149" for Silicon)
Returns:
JSON with comprehensive material properties
| Name | Required | Description | Default |
|---|---|---|---|
| material_id | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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 it returns 'JSON with comprehensive material properties', which gives some output context, but lacks details on permissions, rate limits, error handling, 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 appropriately sized and front-loaded, with a clear purpose statement followed by concise sections for Args and Returns. Every sentence earns its place, providing essential information without unnecessary elaboration, making it efficient 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 tool's low complexity (1 parameter), no annotations, and the presence of an output schema (which handles return values), the description is reasonably complete. It covers the purpose, parameter semantics, and output format, though it could benefit from more behavioral context or usage guidelines to be fully comprehensive.
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 meaningful semantics beyond the input schema, which has 0% description coverage. It explains that 'material_id' is a 'Materials Project ID' and provides an example ('mp-149' for Silicon), clarifying the parameter's purpose and format, which compensates well for the schema's lack of documentation.
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 resource 'detailed properties for a specific material from Materials Project', making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_structure' or 'get_elastic_properties', which might retrieve similar but more specific data.
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 siblings like 'search_materials' for finding materials or 'get_elastic_properties' for specific property subsets, leaving the agent to infer usage context without explicit direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find materials with similar crystal structures.
Args:
material_id: Materials Project ID to find similar structures for
max_results: Maximum number of similar structures (default: 5)
Returns:
JSON with structurally similar materials
| Name | Required | Description | Default |
|---|---|---|---|
| material_id | Yes | ||
| max_results | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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 mentions the tool 'finds' similar structures and returns JSON, but lacks details on behavioral traits such as rate limits, error handling, authentication needs, or what 'similar' means (e.g., similarity metrics or thresholds). This is inadequate for a tool with potential computational 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 appropriately sized and front-loaded, starting with the core purpose. The Args and Returns sections are structured clearly, but the 'Returns' line is somewhat vague ('JSON with structurally similar materials') and could be more specific. Overall, it's efficient with minimal waste.
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 has an output schema, the description doesn't need to detail return values. However, with no annotations, 0% schema coverage, and two parameters, it should provide more context on usage and behavior. It covers basics but lacks depth for a tool that might involve complex structural analysis.
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 0%, so the description must compensate. It adds meaning by explaining 'material_id' as 'Materials Project ID to find similar structures for' and 'max_results' as 'Maximum number of similar structures (default: 5)', which clarifies beyond the schema's basic types. However, it doesn't cover constraints like ID formats or result limits, leaving gaps.
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: 'Find materials with similar crystal structures.' It specifies the verb ('Find') and resource ('materials with similar crystal structures'), making it understandable. However, it doesn't explicitly differentiate from sibling tools like 'compare_materials' or 'search_by_elements,' which might also involve structural comparisons, leaving some ambiguity about uniqueness.
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 prerequisites, exclusions, or compare it to siblings like 'compare_materials' or 'search_by_elements,' which could be relevant for structural queries. Usage is implied through the parameters but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the crystal structure for a material from Materials Project.
Args:
material_id: Materials Project ID (e.g., "mp-149" for Silicon)
format: Output format - "cif", "poscar", or "json" (default: "cif")
Returns:
Crystal structure in the requested format
| Name | Required | Description | Default |
|---|---|---|---|
| material_id | Yes | ||
| format | No | cif |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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 describes the tool's function and output format options, but lacks details on rate limits, authentication needs, error handling, or data freshness. It adequately covers basic behavior but misses advanced operational context.
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 front-loaded with the core purpose, followed by clear sections for arguments and returns. Every sentence adds value—no redundancy or fluff—making it efficiently structured and easy to parse.
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 moderate complexity (2 parameters, no annotations, but with an output schema), the description is mostly complete. It explains parameters and return values well, but could improve by addressing behavioral aspects like rate limits or errors, though the output schema reduces the need for detailed return value explanations.
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 significant meaning beyond the input schema, which has 0% description coverage. It explains that 'material_id' is a Materials Project ID with an example ('mp-149' for Silicon) and defines 'format' options ('cif', 'poscar', 'json') with a default, fully compensating for the schema's lack of documentation.
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 the crystal structure') and resource ('for a material from Materials Project'), distinguishing it from sibling tools that focus on properties, comparisons, or searches rather than retrieving structural data.
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 implies usage by specifying the tool's purpose and parameters, but does not explicitly state when to use this tool versus alternatives like 'get_similar_structures' or 'search_by_elements', nor does it mention prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for materials by band gap range.
Args:
min_gap: Minimum band gap in eV (default: 0)
max_gap: Maximum band gap in eV (default: 10)
direct_gap_only: Only return materials with direct band gaps
max_results: Maximum number of results (default: 10)
Returns:
JSON with materials in the specified band gap range
| Name | Required | Description | Default |
|---|---|---|---|
| min_gap | No | ||
| max_gap | No | ||
| direct_gap_only | No | ||
| max_results | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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 the tool returns JSON with materials, but lacks details on permissions, rate limits, error handling, or whether this is a read-only operation. For a search 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 well-structured with clear sections for Args and Returns, making it easy to scan. It's appropriately sized with no wasted sentences, though the 'Returns' section could be slightly more detailed (e.g., format of materials data). Overall, it's efficient and front-loaded with the core 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 tool's moderate complexity (4 parameters, no annotations, but has output schema), the description is reasonably complete. It covers the purpose, all parameters, and return type. The output schema existence means it doesn't need to detail return values, but it could benefit from more behavioral context (e.g., search scope or 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 description adds substantial meaning beyond the input schema, which has 0% description coverage. It explains all four parameters: 'min_gap' and 'max_gap' are defined with units (eV) and defaults, 'direct_gap_only' clarifies its filter effect, and 'max_results' specifies its purpose. This fully compensates for the schema's lack of descriptions.
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 materials by band gap range.' This specifies the verb ('search') and resource ('materials') with the key criterion ('band gap range'). However, it doesn't explicitly differentiate from sibling tools like 'search_by_elements' or 'search_by_elastic_properties,' 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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools or contexts where band gap searching is appropriate versus other search methods. The only implied usage is for materials with specific band gap properties, but this is too vague 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.
Search for materials by elastic/mechanical properties.
Args:
min_bulk_modulus: Minimum bulk modulus in GPa
max_bulk_modulus: Maximum bulk modulus in GPa
min_shear_modulus: Minimum shear modulus in GPa
max_shear_modulus: Maximum shear modulus in GPa
max_results: Maximum number of results (default: 10)
Returns:
JSON with materials matching the elastic property criteria
| Name | Required | Description | Default |
|---|---|---|---|
| min_bulk_modulus | No | ||
| max_bulk_modulus | No | ||
| min_shear_modulus | No | ||
| max_shear_modulus | No | ||
| max_results | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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 the tool returns JSON with matching materials, which is helpful, but doesn't describe important behaviors like pagination (only mentions max_results default), error conditions, rate limits, authentication requirements, or whether this is a read-only operation. The description is minimal beyond basic functionality.
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 perfectly structured and concise. It starts with a clear purpose statement, then provides a well-organized 'Args' section with bullet-point explanations, followed by a 'Returns' section. Every sentence earns its place with no wasted words or redundancy.
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 has 5 parameters with 0% schema description coverage but an output schema exists, the description does a good job explaining parameters but is incomplete behaviorally. It adequately covers the search functionality and parameters but lacks context about when to use it versus siblings, behavioral constraints, and doesn't need to explain return values since an output schema exists.
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 provides excellent parameter semantics despite 0% schema description coverage. It clearly explains what each parameter represents (minimum/maximum bulk/shear modulus in GPa, maximum results with default), adding crucial meaning beyond the schema's generic titles. This fully compensates for the lack of schema descriptions, though it doesn't explain the nullability indicated in the schema.
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 searches for materials by elastic/mechanical properties, providing a specific verb ('search') and resource ('materials'). It distinguishes from some siblings like 'get_elastic_properties' (which likely retrieves properties for specific materials) but doesn't explicitly differentiate from other search tools like 'search_by_band_gap' or 'search_by_elements'.
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 about when to use this tool versus alternatives. While the purpose implies it's for searching by elastic properties, there's no mention of when to choose this over other search tools (like 'search_by_band_gap' or 'search_by_elements') or when to use it versus 'get_elastic_properties' (which might retrieve properties for known materials).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for materials containing specific elements.
Args:
elements: List of elements that must be present (e.g., ["Li", "Fe", "O"])
exclude_elements: Optional list of elements to exclude
max_results: Maximum number of results (default: 10)
Returns:
JSON with matching materials
| Name | Required | Description | Default |
|---|---|---|---|
| elements | Yes | ||
| exclude_elements | No | ||
| max_results | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavior. It mentions the tool returns 'JSON with matching materials', which gives some output context, but lacks details on permissions, rate limits, error handling, or whether it's read-only. This is inadequate for a search 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 well-structured and concise, with a clear purpose statement followed by bullet-like sections for 'Args' and 'Returns'. Every sentence adds value without redundancy, making it easy to parse.
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 moderate complexity, no annotations, and an output schema (implied by 'Returns: JSON'), the description is mostly complete. It covers parameters well but lacks behavioral details like error cases or performance limits, which holds it back from a score of 5.
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 significant meaning beyond the input schema, which has 0% description coverage. It explains each parameter's purpose with examples (e.g., elements as a list like ['Li', 'Fe', 'O']) and default values, compensating well for the schema's lack of descriptions.
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 materials containing specific elements.' It specifies the verb ('search') and resource ('materials'), but does not explicitly differentiate it from sibling tools like 'search_by_band_gap' or 'search_materials', which prevents a score of 5.
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 sibling tools or contexts where this search is preferred over others, such as 'search_by_band_gap' or 'search_materials', leaving the agent without usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for materials by chemical formula in the Materials Project database.
Args:
formula: Chemical formula (e.g., "Fe2O3", "LiFePO4", "Si")
max_results: Maximum number of results to return (default: 10)
Returns:
JSON with matching materials including material_id, formula, and key properties
| Name | Required | Description | Default |
|---|---|---|---|
| formula | Yes | ||
| max_results | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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 the database source and return format, but doesn't cover important aspects like rate limits, authentication requirements, pagination behavior, or error conditions. The description adds some context but leaves significant gaps for a search tool.
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 efficiently structured with clear sections: purpose statement, parameter documentation, and return format. Every sentence adds value without redundancy. The formatting with 'Args:' and 'Returns:' sections enhances readability while maintaining brevity.
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 has an output schema (which handles return values) and only 2 parameters with good semantic coverage in the description, the description is reasonably complete. However, as a search tool with no annotations, it should ideally mention more behavioral aspects like result ordering, pagination, or common use cases to be fully comprehensive.
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 meaningful semantic information beyond the input schema. While schema description coverage is 0%, the description provides concrete examples for the 'formula' parameter ('Fe2O3', 'LiFePO4', 'Si') and clarifies the default value and purpose of 'max_results'. This compensates well for the lack of schema descriptions.
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 materials by chemical formula in the Materials Project database.' It specifies the verb ('search'), resource ('materials'), and scope ('Materials Project database'), but doesn't explicitly differentiate from sibling tools like 'search_by_band_gap' or 'search_by_elements'.
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 sibling tools like 'search_by_band_gap' or 'search_by_elements', nor does it specify prerequisites or exclusions. Usage is implied by the purpose statement alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each tool has a clearly distinct purpose with no significant overlap. For example, get_properties provides comprehensive details, while get_elastic_properties focuses specifically on mechanical properties, and get_structure returns crystal structure data. The search tools target different criteria (elements, band gap, elastic properties, formula), making tool selection straightforward.
All tools follow a consistent verb_noun naming pattern using snake_case. The pattern is uniform throughout: compare_materials, get_elastic_properties, get_phase_diagram, get_properties, get_similar_structures, get_structure, search_by_band_gap, search_by_elastic_properties, search_by_elements, and search_materials. This consistency makes the tool set predictable and easy to navigate.
With 10 tools, the server is well-scoped for materials science data retrieval. Each tool serves a specific function in querying and analyzing materials data, from basic searches to detailed property comparisons. The count aligns with typical MCP servers and covers the domain without being overwhelming or insufficient.
The tool set provides strong coverage for materials data retrieval, including search, get, and compare operations. However, there are minor gaps, such as no tools for updating or deleting data (though this may be intentional if read-only) and no specific tools for advanced analyses like thermodynamic stability or electronic structure beyond band gaps. Overall, it supports core workflows effectively.
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