A stateless MCP server providing evidence-based strength training tools that encode classical powerlifting programs, fatigue modeling, and training science principles — with all state managed by the calling agent.
Browse training programs (list_training_templates): Explore a built-in library of classical programs (5/3/1, Texas Method, Madcow, GZCLP, nSuns CAP3, Coan-Philippi, Smolov Jr), filterable by category (powerlifting, strength, peaking) and difficulty (beginner, intermediate, advanced).
Retrieve weekly training plans (get_template_plan): Get a specific week's prescribed sessions from any template, including exercises, sets, reps, intensity, and AMRAP flags.
Look up exercise form guidance (lookup_exercise_form): Get form cues, common mistakes, and equipment-filtered alternatives for any exercise.
Learn training science principles (explain_principle): Get cited explanations of concepts like RPE autoregulation, periodization, the Banister model, deload triggers, and volume landmarks.
Calculate fatigue scores (calculate_fatigue_score): Compute Banister fitness-fatigue metrics (CTL/ATL/TSB) from recent training history, optionally incorporating recovery data like sleep and soreness.
Get session modification suggestions (suggest_session_modification): Receive actionable adjustment recommendations (scale weight, change intensity, deload, etc.) based on planned vs. actual performance and current fatigue state.
Apply plan adjustments (apply_plan_adjustment): Modify a week's plan by applying adjustments such as DELOAD_WEEK, SCALE_WEEK, SHIFT_VOLUME, or ADD_REST_DAY, returning the adjusted plan JSON.
Get today's session recommendation (recommend_session_for_today): Receive a personalized session recommendation with rationale based on your template, current week, fatigue state, and last session.
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., "@strength-training-mcp Recommend today's session with fatigue score -15."
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 stateless MCP server exposing 8 tools for evidence-based strength training. Encodes classical powerlifting programs (5/3/1, Texas Method, Madcow, GZCLP, nSuns CAP3, Coan-Philippi, Smolov Jr), the Banister fitness-fatigue model, RPE-based autoregulation, and an adjustment policy engine.
No user data is stored on the server. All state lives in the calling agent. The server is a pure function: same inputs → same outputs.
Transport | Support | Entry Point | Use Case |
Stdio | ✅ |
| Claude Desktop, Claude Code, Cursor |
Streamable HTTP | ✅ |
| ModelScope, Aura, remote agents |
SSE | ❌ | — | Not implemented (use Streamable HTTP instead) |
Related MCP server: RepStack
uvx --from strength-training-mcp strength-training-mcpuvx --from strength-training-mcp strength-training-mcp-http --port 8080Test:
curl http://localhost:8080/health
# → {"status":"ok","version":"0.1.1"}For MCP calls over HTTP, use any MCP client (e.g., fastmcp.Client, Claude Desktop, or ModelScope) pointing at http://localhost:8080/mcp.
This package is published to PyPI as strength-training-mcp. ModelScope can deploy it directly via uvx.
In ModelScope MCP deployment console, select Stdio or Streamable HTTP.
Recommendation: Use Stdio for the simplest one-click deployment.
{
"mcpServers": {
"strength-training": {
"command": "uvx",
"args": [
"--from",
"strength-training-mcp",
"strength-training-mcp"
],
"env": {}
}
}
}Deploy the HTTP server first (see Self-Host below), then fill your public URL:
{
"mcpServers": {
"strength-training": {
"type": "http",
"url": "https://your-domain.com/mcp"
}
}
}This server requires no API keys, no environment variables, and no database. Leave parameter config empty.
After deployment, test:
curl https://your-deployment-url/health
# → {"status":"ok","version":"0.1.1"}See docs/selfhost.md for:
systemd service setup
nginx reverse proxy (hide application port)
Caddy + HTTPS
Docker deployment
Minimal production setup:
# Install
uv tool install strength-training-mcp
# Run behind nginx on port 80
strength-training-mcp-http --host 127.0.0.1 --port 3492Then configure nginx to proxy 80 → 127.0.0.1:3492.
Tool | Purpose |
| Browse the built-in program library |
| Get a specific week's prescribed sessions |
| Get form cues + alternatives for an exercise |
| Explain a training science principle with citation |
| Compute Banister CTL/ATL/TSB from training history |
| Get adjustment recommendations based on fatigue + actual |
| Apply aggregate adjustments to a week (deload, etc.) |
| Compose today's session with rationale |
See docs/api.md for full tool reference, input schemas, and error codes.
{
"mcpServers": {
"strength-training": {
"command": "uvx",
"args": [
"--from",
"strength-training-mcp",
"strength-training-mcp"
]
}
}
}uv sync --all-extras
uv run pytest tests/unit # unit tests
uv run pytest tests/integration # E2E tests
uv run pytest --cov=src/strength_training_mcpAll templates and principles cite their original public sources. See docs/rts-principles.md for citations.
MIT
Given a template, target week, and adjustments (DELOAD_WEEK, SCALE_WEEK, SHIFT_VOLUME, ADD_REST_DAY), return the adjusted plan JSON. The agent decides whether to persist this — the user retains veto power.
| Name | Required | Description | Default |
|---|---|---|---|
| template_id | Yes | ||
| week_number | Yes | ||
| adjustments | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must carry burden. It indicates the tool returns a plan JSON without persisting it, implying no side effects. However, no details on authentication, rate limits, or error conditions are given.
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?
Two concise sentences with no extraneous information. The first sentence covers the core function, the second adds important context about decision process.
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 no output schema and no annotations, the description lacks detail on the returned JSON structure and the effects of each adjustment type. It adequately identifies inputs but fails to describe tool behavior comprehensively.
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 baseline is 4. The description lists the adjustment types, which adds value beyond the schema's enum. However, it does not explain the other fields (reason, deltas) or how adjustments modify the plan, thus only partially compensates.
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?
Clearly states the tool applies adjustments to a plan template and returns the adjusted plan JSON. Mentions the four adjustment types. Distinguishes from siblings like suggest_session_modification or get_template_plan by focusing on plan-level adjustments.
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 note about persistence and user veto provides some context but fails to clarify scenarios or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Calculate Banister fitness-fatigue metrics (CTL/ATL/TSB) from recent training sets. Returns MISSING_BASELINE if sets is empty (constraint C4). Provide at least 14 days for partial CTL/ATL, 42 days recommended for full.
| Name | Required | Description | Default |
|---|---|---|---|
| sets | Yes | Recent training sets. Provide ≥42 days for full CTL/ATL accuracy. | |
| recovery | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It reveals the tool returns 'MISSING_BASELINE' if sets are empty, implying a constraint. It does not discuss read-only nature, side effects, auth needs, or output format, but the behavior is partially disclosed.
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?
Two sentences achieve high information density: first sentence states purpose, second sentence adds usage guidelines and error constraint. No superfluous content, front-loaded.
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 output schema, the description lacks details about the return format (e.g., CTL, ATL, TSB values). It covers input requirements and one error scenario, but for a calculation tool, output description would enhance completeness. Still fairly complete for its complexity.
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 context to the 'sets' parameter by specifying recommended day counts (14/42), which goes beyond the schema's brief 'Recent training sets. Provide ≥42 days for full CTL/ATL accuracy.' The 'recovery' parameter has no schema description but the tool description does not mention it. Overall, value added.
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 'Calculate' and the resource 'Banister fitness-fatigue metrics (CTL/ATL/TSB) from recent training sets'. It is distinct from sibling tools like 'suggest_session_modification' or 'recommend_session_for_today', which have different purposes.
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 implicit usage guidance by recommending data lengths ('Provide at least 14 days for partial CTA/L, 42 days recommended for full') and noting a specific error condition for empty input. However, it does not explicitly compare with siblings or state when not to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Explain a training science principle with source citation. Topics include: rpe_autoregulation, dup_periodization, banister_model, deload_triggers, volume_landmarks, etc. Returns body + source_citation + related_tools.
| Name | Required | Description | Default |
|---|---|---|---|
| topic | Yes | Principle ID (see /docs/rts-principles.md for full list) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description discloses return fields (body, source_citation, related_tools) beyond the input schema, providing useful behavioral context. No annotations to complement or contradict.
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?
Two sentences succinctly convey purpose, examples, and output, with no wasted words. Front-loaded with the core action.
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 simple lookup with one parameter and no output schema, the description adequately covers purpose, examples, and return structure, though it could explicitly differentiate from siblings.
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 single parameter 'topic' has a description in the schema referencing an external doc; the tool description adds example topics and return info, but adds limited extra meaning beyond schema coverage of 100%.
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?
Description clearly states it explains a training science principle with source citation, listing example topics and return structure, effectively distinguishing from siblings like 'recommend_session_for_today' and 'lookup_exercise_form'.
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. While example topics are given, there is no mention of prerequisites or when to choose it over sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get a specific week's sessions from a template. Pure function: same input returns same output. Returns a list of sessions with prescribed exercises (sets, reps, intensity, AMRAP flag).
| Name | Required | Description | Default |
|---|---|---|---|
| template_id | Yes | Template ID from list_training_templates | |
| week_number | Yes | Which week of the cycle (1-indexed) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Explicitly states 'Pure function: same input returns same output,' which discloses deterministic behavior and lack of side effects. This compensates for missing annotations, though error handling or access constraints are not mentioned.
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?
Two sentences, zero waste. First sentence states purpose, second adds behavioral and output details. Information is front-loaded and every word earns its place.
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?
Describes output structure (list of sessions with sets, reps, etc.) despite no output schema. Covers key inputs and behavior. Missing edge cases like missing template or invalid week, but sufficient for a simple get operation.
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 provides 100% coverage with clear descriptions (e.g., 'Template ID from list_training_templates'). Description adds no extra meaning beyond what schema already conveys, so baseline 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?
Description clearly states 'Get a specific week's sessions from a template.' It uses a specific verb and resource, distinguishing it from sibling tools like list_training_templates (list) and others that modify or analyze.
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 vs alternatives. The 'Pure function' note hints at idempotency but doesn't define usage context or prerequisites. Implied usage is retrieving planned sessions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all built-in training templates. Returns catalog of classical strength/powerlifting programs with metadata (id, name, author, weeks, days_per_week, difficulty, category, source_url).
| Name | Required | Description | Default |
|---|---|---|---|
| category | No | Filter by program category | |
| difficulty | No | Filter by difficulty |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description discloses that it returns a catalog with specific metadata fields (id, name, author, etc.). It does not mention pagination or performance but is sufficient for a list operation.
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?
Two concise sentences with no fluff. The first sentence states the action, and the second details the output. Ideal structure.
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 low-complexity tool with two optional parameters, the description fully covers purpose and return fields. No output schema exists, but the description lists the metadata fields, making it complete.
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, so baseline is 3. The description adds value by listing the return fields, which compensates for no output 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 explicitly states 'List all built-in training templates' with a clear verb and resource. It distinguishes from siblings like get_template_plan by specifying it returns a catalog of templates.
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 for browsing templates but lacks explicit when-to-use vs. alternatives or when not to use it. No guidance on selecting this over similar tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get form cues, common mistakes, and equipment alternatives for an exercise. Returns cues list, common_mistakes list, and alternatives list.
| Name | Required | Description | Default |
|---|---|---|---|
| exercise_name | Yes | Exercise name or ID | |
| equipment | No | Filter alternatives by equipment |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
While the tool name suggests a read-only operation, the description does not explicitly state that it is non-destructive or require specific permissions. It only describes the return structure (three lists) but offers no other behavioral details. Without annotations, the description carries full burden, and this is an average disclosure.
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—two sentences totaling 16 words—with no redundant information. Every word contributes meaning, 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 tool's simplicity (2 parameters, no output schema, no annotations), the description adequately covers return types and general purpose. It lacks details on error handling or behavior for missing exercises, but is mostly complete for basic 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?
Both parameters (exercise_name, equipment) have descriptions in the input schema, achieving 100% coverage. The tool description does not add further meaning beyond what the schema already provides, so a 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: retrieving form cues, common mistakes, and equipment alternatives for an exercise. It uses a specific verb-resource structure and is distinct from all sibling tools, which focus on plans, fatigue, principles, templates, and sessions.
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 for looking up exercise form but provides no explicit guidance on when to use this tool versus alternatives, nor does it mention any prerequisites or exclusions. For a straightforward lookup, this is minimally adequate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Given a template, current week, fatigue state, and last session, return today's recommended session with rationale (e.g., deload triggered).
| Name | Required | Description | Default |
|---|---|---|---|
| template_id | Yes | ||
| current_week | Yes | ||
| fatigue | Yes | ||
| last_session | No |
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 indicates the tool is read-only (returns recommendation), but does not disclose side effects, authentication needs, or rate limits. The mention of 'deload triggered' gives a hint of logic but not sufficient behavioral detail.
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, compact sentence of about 20 words. It front-loads the purpose and context, and includes a concrete example ('deload triggered'). No wasted or redundant words.
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 4 parameters, no output schema, and no annotations, the description is too brief. It does not specify the return structure or format of the rationale. For a tool that likely returns a complex session recommendation, this leaves significant ambiguity.
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% (no descriptions on individual parameters). The description only names the parameters generically: 'template, current week, fatigue state, and last session'. It does not clarify the structure of 'fatigue' (a loosely typed object) or expected format of 'last_session'. This adds minimal value beyond the schema's property names.
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 it returns a recommended session with rationale, using inputs like template, week, fatigue, and last session. This distinguishes it from siblings like 'calculate_fatigue_score' and 'get_template_plan', though 'suggest_session_modification' could overlap. The example 'deload triggered' adds specificity.
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 implies it is for generating a recommendation when you have template, week, fatigue, and last session, but does not mention prerequisites, when not to use it, or how it relates to siblings like 'calculate_fatigue_score' or 'suggest_session_modification'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Given a planned session, what was actually performed, and current fatigue state, return a list of suggested adjustments (scale weight, change intensity, deload, etc.). Returns {adjustments: [...], summary: '...'}.
| Name | Required | Description | Default |
|---|---|---|---|
| planned | Yes | ||
| actual | No | ||
| fatigue | 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 describes the return format but lacks disclosure of side effects, authentication needs, or whether it is read-only. The impact is moderate; it suggests a non-destructive analysis 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 sentence that front-loads the inputs and output. It is concise and to the point, with no wasted words.
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 (3 parameters, nested objects, no output schema), the description provides a high-level overview but lacks details on parameter structures, defaults, and return format beyond the example. It is minimally adequate but leaves gaps for the agent to infer.
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 explains the three inputs in natural language ('planned session', 'what was actually performed', 'current fatigue state'), which adds basic meaning. However, it does not detail the nested structure or optionality of 'actual' (default null), nor does it describe the schema definitions. Partially compensates but not fully.
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 returns a list of suggested adjustments based on planned session, actual performance, and fatigue state. It implies differentiation from siblings like 'recommend_session_for_today' or 'apply_plan_adjustment', though explicit sibling distinction is not provided.
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 gives context on when to use (when you have planned, actual, fatigue data) but does not explicitly state when not to use or mention alternatives. It provides moderate guidance.
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.
v0.1.1apply_plan_adjustmentcalculate_fatigue_scoreexplain_principleget_template_planlist_training_templateslookup_exercise_formrecommend_session_for_todaysuggest_session_modificationEach tool has a clearly distinct purpose: listing templates, getting plans, adjusting plans, calculating fatigue, explaining principles, looking up exercises, recommending sessions, and suggesting modifications. There is no ambiguity between tools.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., list_training_templates, calculate_fatigue_score). No mixing of styles or vague verbs.
With 8 tools, the server is well-scoped for strength training planning and education. Neither too few nor too many for the domain.
The tool set covers core operations: template listing, plan retrieval/adjustment, fatigue calculation, session recommendation/modification, and educational resources. Missing features like template creation or progress logging but these are minor gaps.
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