Provides tools for training, inspecting, validating, testing, predicting, and checkpointing PyTorch Lightning models.
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., "@PyTorch Lightning MCP Server train a ResNet-18 on CIFAR-10 dataset"
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 minimal integration layer exposing PyTorch Lightning via a structured, machine-readable API for tools, agents, and orchestration systems.
Structured APIs for training, inspecting, validating, testing, predicting, and checkpointing models
PyTorch Lightning execution
Stdio and HTTP server modes
Related MCP server: MCP Server with External Tools
Python 3.10–3.12
PyTorch Lightning (compatible version)
uv (recommended for dependency management)
curl -Ls https://astral.sh/uv/install.sh | sh
git clone https://github.com/<your-org>/lightning-mcp.git
cd lightning-mcp
uv sync --all-extrasYou can run the MCP server via CLI:
# Stdio server (default)
uv run lightning-mcp
# HTTP server
uv run lightning-mcp --http --host 0.0.0.0 --port 3333echo '{"id":"1","method":"lightning.inspect","params":{"what":"environment"}}' | uv run lightning-mcpcurl -X POST http://localhost:3333/mcp \
-H "Content-Type: application/json" \
-d '{"id":"1","method":"lightning.inspect","params":{"what":"environment"}}'The MCP server exposes the following tools (methods):
lightning.trainTrain a PyTorch Lightning model with explicit configuration.
Input schema:
{
"model": {"_target_": "string", ...},
"trainer": { ... }
}lightning.inspectInspect a model or the runtime environment.
Input schema:
{
"what": "model | environment | summary",
"model": {"_target_": "string", ...} // required for model inspection
}lightning.validateValidate a PyTorch Lightning model.
Input schema:
{
"model": {"_target_": "string", ...},
"trainer": { ... }
}lightning.testTest a PyTorch Lightning model.
Input schema:
{
"model": {"_target_": "string", ...},
"trainer": { ... }
}lightning.predictRun prediction/inference with a PyTorch Lightning model.
Input schema:
{
"model": {"_target_": "string", ...},
"trainer": { ... }
}lightning.checkpointManage model checkpoints: save, load, or list.
Input schema:
{
"action": "save | load | list",
"path": "string", // for save/load
"directory": "string", // for list
"model": { ... } // for save/load
}To list all available tools and their schemas at runtime:
echo '{"id":"1","method":"tools/list","params":{}}' | uv run lightning-mcpuv run pytestSee CONTRIBUTING.md and DEVELOPMENT.md.
Apache 2.0
Manage model checkpoints: save, load, or list.
| Name | Required | Description | Default |
|---|---|---|---|
| path | No | Checkpoint file path (for save/load). | |
| model | No | Model configuration (for save/load). | |
| action | Yes | Action to perform. | |
| directory | No | Directory to list checkpoints from. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full responsibility for behavioral disclosure. It only lists actions (save, load, list) without mentioning side effects, resource implications, permissions, or return behavior. Important details like file creation, overwrite behavior, or required permissions are omitted.
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, concise sentence that immediately conveys the core purpose. It front-loads the verb and resource, with the colon providing a clear separation of action enumeration.
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 4 parameters, nested objects, and no output schema, the description is too minimal. It does not explain when to use each action, what the model object requires, or what return values look like. The multi-action nature demands more context for correct agent invocation.
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?
Input schema has 100% coverage with descriptions for all 4 parameters. The tool description does not add any extra meaning beyond the schema, so the baseline score of 3 is appropriate. No additional context about parameter usage or relationships is provided.
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 as managing model checkpoints with three specific actions: save, load, or list. It distinguishes itself from sibling tools like inspect, predict, train, etc., by focusing on checkpoint operations.
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 only lists actions, but does not specify appropriate contexts, prerequisites, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Inspect models or runtime environment.
| Name | Required | Description | Default |
|---|---|---|---|
| what | Yes | Inspection target (model, environment, summary). | |
| model | No | Model configuration (required for model inspection). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It only says 'Inspect' without disclosing read-only nature, permissions, or error behavior. Minimal transparency.
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 concise (one short sentence) but omits the 'summary' inspection target. It is adequate but could be more complete without losing conciseness.
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?
Lacks details about return format or behavior. With no output schema and no annotations, the description should provide more context on what inspection produces (e.g., JSON, console 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 coverage is 100% and parameter descriptions are clear. The description adds no new parameter information beyond summarizing the overall purpose, which is redundant with 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 states 'Inspect models or runtime environment' but the 'what' parameter includes 'summary' as a target, making the description incomplete. It does not distinguish from sibling tools like lightning.checkpoint, which may also involve inspection.
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 on when to use this tool versus alternatives. The description lacks explicit when/when-not statements, leaving the agent to infer usage context from sibling tool names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run prediction/inference with a PyTorch Lightning model.
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | Model configuration (_target_ + kwargs). | |
| trainer | No | Trainer configuration. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description bears full burden for behavioral disclosure. It merely states the tool runs prediction/inference, without revealing side effects, state changes, resource requirements, or output characteristics. This is insufficient for an inference 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 a single, efficient sentence containing no filler words. Every word contributes to defining the tool's 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 has nested object parameters (model and trainer) and no output schema, the description is too terse. It lacks details on expected configuration formats, defaults, or return behavior, making it incomplete for an agent to use 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 coverage is 100% (both parameters described in schema). The description adds no extra meaning beyond the schema's descriptions of 'model configuration' and 'trainer configuration.' Baseline 3 is appropriate since schema fulfills basic needs.
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 as running prediction/inference with a PyTorch Lightning model, using a specific verb and resource. It distinguishes well from sibling tools like lightning.train, lightning.test, and lightning.validate, which focus on training, testing, and validation.
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 such as lightning.test or lightning.validate. It does not mention prerequisites, common scenarios, or exclusions, leaving the agent to infer appropriate use from context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Test a PyTorch Lightning model.
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | Model configuration (_target_ + kwargs). | |
| trainer | No | Trainer configuration. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description fails to disclose any behavioral details such as side effects, permissions, or return values. With no annotations, the description should provide this context but does not.
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 overly brief (one sentence) and does not adequately specify the tool's functionality. It sacrifices completeness for brevity, resulting in under-specification.
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 (nested objects, no output schema) and lack of annotations, the description is completely insufficient. It does not explain the testing process, expected outputs, or any prerequisites.
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 coverage is 100% with descriptions for both parameters. The description adds no additional meaning beyond what the schema already provides, meeting the baseline for high 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 'Test a PyTorch Lightning model' uses a specific verb and resource, making the basic purpose clear. However, it does not differentiate from sibling tools like 'validate' or 'predict', which could overlap in meaning.
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 'lightning.train' or 'lightning.validate'. The description lacks any context for appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Train a PyTorch Lightning model with explicit configuration.
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | Model configuration (_target_ + kwargs). | |
| trainer | No | Trainer configuration. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must bear the full burden of disclosure. It only says 'Train' without any details on side effects, state changes, permissions, or what happens after execution. This is insufficient for an AI 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, front-loaded sentence. It is concise but may be too brief, sacrificing necessary detail for 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's complexity (nested objects, no output schema, no annotations) the description is vastly incomplete. It fails to explain return values, execution flow, error handling, or usage patterns.
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 covers both parameters with descriptions (coverage 100%), so the baseline is 3. The tool description adds no additional semantic meaning beyond the schema's 'Model configuration (_target_ + kwargs)' and 'Trainer configuration'.
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 'Train' and the resource 'PyTorch Lightning model'. It implicitly differentiates from sibling tools like 'validate' and 'predict' by the name itself. However, it lacks additional context to fully distinguish its role among related operations.
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 on when to use this tool versus alternatives (e.g., validate, test). No prerequisites or exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Validate a PyTorch Lightning model.
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | Model configuration (_target_ + kwargs). | |
| trainer | No | Trainer configuration. |
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 does not disclose what validation entails (e.g., runs a forward pass on validation data, computes metrics, requires a model and optional trainer) or any side effects (e.g., no model updates, read-only). The agent is left guessing about 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 extremely concise at one sentence, which is beneficial for front-loading. However, it sacrifices completeness for brevity; a few more sentences could improve clarity without being verbose. No structural issues.
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?
With nested objects, no output schema, and no annotations, the description should compensate by explaining what validation does, what the parameters accept in terms of structure (e.g., model config as _target_ + kwargs), and what the tool returns. Currently it is too minimal for an AI agent to use correctly.
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% (both 'model' and 'trainer' have descriptions in the schema), so the description adds no additional meaning beyond 'Model configuration' and 'Trainer configuration'. Baseline 3 is appropriate as the schema already handles parameter semantics 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 verb 'Validate' and the resource 'a PyTorch Lightning model', distinguishing it from sibling tools like lightning.train and lightning.predict. However, it could be more specific by mentioning that validation runs on a validation dataset or computes performance metrics.
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. The description does not specify context like 'use after training to evaluate model performance on validation data' or mention prerequisites, making it difficult for an AI agent to choose correctly among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each tool targets a distinct stage of the PyTorch Lightning model lifecycle (train, validate, test, predict, checkpoint, inspect), with no overlap in functionality.
All tools follow a consistent 'lightning.<action>' pattern (e.g., lightning.train, lightning.test), making it easy to infer purpose from name.
6 tools cover the essential operations for a PyTorch Lightning workflow without being excessive or insufficient.
The set covers the main lifecycle stages, though advanced features like hyperparameter tuning or model export are absent; still satisfactory for core usage.
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