ShowMeStepByStep
Server Details
Search, browse, and cite a 377-tutorial step-by-step how-to corpus from your AI client.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4/5 across 6 of 6 tools scored.
Each tool has a clearly distinct purpose: fetching a tutorial by slug, listing categories, recent tutorials, tutorials by category, submitting a request, and searching. No overlap in functionality.
All tools follow a consistent verb_noun pattern in snake_case (e.g., list_categories, search_tutorials). No mixing of conventions or vague verbs.
With 6 tools, the server is well-scoped. It covers the essential operations for a tutorial discovery service (search, browse, details, request) without unnecessary bloat.
The tool set covers the full lifecycle needed: search, browse by category and recency, fetch details, and request missing content. No dead ends or missing operations for the stated purpose.
Available Tools
6 toolsget_tutorialAInspect
Fetch the full machine-readable rendition of a tutorial by slug: title, TL;DR, numbered steps (with text, imageUrl, tips, and YouTube videoTimestampSeconds for deep-linking), tools/materials, tags, related tutorial slugs, creator credit, recipe (with nutrition + cuisine when applicable), license, and canonicalUrl. Use this after search_tutorials to fetch step details.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Tutorial slug (e.g. 'how-to-fix-a-leaky-faucet') |
Tool Definition Quality
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 output comprehensively but does not disclose any potential side effects, authentication requirements, rate limits, or error conditions. For a straightforward read operation, this is adequate but not exhaustive.
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 two sentences, front-loaded with a comprehensive list of returned fields, followed by a concise usage instruction. Every word adds value with no 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's simplicity (one required parameter, no output schema), the description fully explains the input, output (listing all fields), and usage context. It is complete for an AI agent to select and invoke this tool 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?
Input schema coverage is 100% with a description for 'slug'. The description adds a usage example ('how-to-fix-a-leaky-faucet') but does not provide significant meaning beyond what the schema already offers. Baseline 3 applies.
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 fetches a machine-readable rendition of a tutorial by slug, enumerating all returned fields (title, steps, etc.). It distinguishes itself from siblings by specifying 'Use this after search_tutorials to fetch step details.'
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 explicitly advises to use this tool after search_tutorials, providing clear sequential context. It does not explicitly mention when not to use or alternatives, but the guidance is sufficient for a simple retrieval tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_categoriesAInspect
List all tutorial categories with a published tutorial. Returns slug, name, short description, and count of published tutorials per category.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description adequately discloses that it is a read-only list operation and specifies the returned fields (slug, name, short description, count).
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?
Single sentence effectively conveys purpose and output details. No redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a parameterless tool with no output schema, the description covers purpose and return fields. It could improve by mentioning the filtering condition (only categories with published tutorials) more prominently.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, so schema coverage is 100%. The description adds meaning by explaining the output content, which is fully adequate.
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 lists tutorial categories with a published tutorial, specifying returned fields. However, it does not differentiate from sibling tools like list_tutorials_by_category or list_recent_tutorials.
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 fetching categories with published tutorials, but provides no explicit guidance on when to use this versus siblings or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_recent_tutorialsAInspect
List the most recently published tutorials, newest first. Use this to discover what's new on the site or to surface fresh content for aggregator / 'what's new this week' agent loops. Tenant-aware.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Lookback window in days (default 14, max 90). | |
| limit | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries the burden. It discloses tenant-awareness and the sorting behavior. Does not cover pagination, rate limits, or auth, which are acceptable for a simple list tool. Adequate but not exhaustive.
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 tightly packed sentences convey purpose, ordering, use cases, and tenant-awareness. 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 read-only list tool with two parameters and no output schema, the description covers purpose, usage, and sorting. Tenant-awareness is a bonus. Slightly higher than 3 due to the explicit use cases and tenant note.
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 50% (only 'days' has extra description). The tool description adds no additional parameter meaning beyond what the schema provides for days, and 'limit' is left undocumented. Baseline 3 since schema partially covers but description adds minimal value.
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 starts with a clear verb ('List'), specific resource ('most recently published tutorials'), and ordering ('newest first'). This directly distinguishes it from siblings like search_tutorials or list_tutorials_by_category.
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?
Explicitly states two use cases: discovering new content and serving aggregator loops. Also mentions 'Tenant-aware,' guiding context-aware usage. Does not contrast with alternatives but provides clear scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_tutorials_by_categoryAInspect
Browse tutorials within a specific category, ranked by view count then publish date. Use this when a user wants to explore a topic area rather than search.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| categorySlug | Yes | e.g. 'cooking', 'home-improvement', 'crafts' |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states the ranking order but omits important traits such as idempotency, pagination behavior, error states for invalid categories, or any requirements for use.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no redundancy. The first sentence defines functionality and ranking, the second gives usage guidance. Efficient but could be more structured with bullet points.
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?
No output schema means description should cover return structure. It mentions ranking but not the fields returned or pagination despite a limit parameter. The guidance on usage is helpful but completeness is moderate.
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 50% (limit lacks description in schema). The description adds value by providing example values for categorySlug and implying its role, but does not explain limit's effect or constraints beyond the schema's default/min/max.
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 browses tutorials within a specific category, and specifies the ranking order (by view count then publish date). It also distinguishes from searching, indicating a specific use case.
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 explicitly advises when to use this tool ('when a user wants to explore a topic area rather than search'), contrasting it with search_tutorials. However, it does not address other alternative sibling tools like list_recent_tutorials or get_tutorial.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
request_tutorialAInspect
Submit a request for a tutorial we don't yet have. Call this when search_tutorials returned no good match for what the user actually wants. The request enters our content-demand queue, which the daily content batch reviews and prioritizes. Optionally include an email for notification when the tutorial ships.
| Name | Required | Description | Default |
|---|---|---|---|
| topic | Yes | What the user wants to learn how to do (e.g. 'how to get pesto stains out of a white shirt') | |
| context | No | Brief context about the requester (e.g. 'beginner crocheter', 'first-time homeowner', 'gluten-free baker'). Helps shape the tutorial when we ship it. | |
| notifyEmail | No | Optional email to notify when the tutorial ships. Pure opt-in. | |
| originalQuery | No | The exact user query that came up empty in search_tutorials. Useful for matching keyword demand. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses the post-submission process (queue, daily review) and optional notification, providing behavioral transparency beyond the schema. However, it does not mention whether the request is immediately accepted or any limits.
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 (3 sentences) with front-loaded purpose, then usage condition, then behavioral details. Every sentence adds value.
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?
The description explains the effect (queue entry) but fails to mention the immediate response (e.g., success confirmation, request ID). With no output schema, this omission reduces completeness.
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 already describes all parameters with 100% coverage. The description mentions the optional email notification but does not significantly add new meaning beyond 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 action 'Submit a request for a tutorial we don't yet have' and explicitly distinguishes usage from sibling search_tutorials by specifying the condition when to call this tool.
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 explicitly states the precondition ('when search_tutorials returned no good match') and implies the alternative (search_tutorials) so the agent knows exactly when to invoke this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_tutorialsAInspect
Search the ShowMeStepByStep tutorial corpus by free-text query. Searches across tutorial titles, descriptions, and step content. Returns slim summaries with TL;DRs and canonical URLs. Tenant-aware. Optional filters narrow by category, difficulty, or maximum duration.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | Search query (>= 2 chars) | |
| difficulty | No | Optional difficulty filter. | |
| categorySlug | No | Optional category to restrict the search to (e.g. 'cooking', 'crochet'). Use list_categories to see available slugs. | |
| maxDurationMinutes | No | Optional ceiling on tutorial total duration. Useful for 'quick recipes' / 'under 10 min' queries. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It discloses return format (slim summaries with TL;DRs, canonical URLs) and mentions tenant-awareness. However, it does not specify pagination, ordering, or behavior for empty results, which are common search behavioral traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, front-loading the core action and then adding return format and filters. Every sentence adds essential information without 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 no output schema, the description explains return format adequately. It covers all filter parameters mentioned in schema. Missing details like pagination or result ordering prevent a perfect score, but overall it is fairly 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 80% with detailed parameter descriptions. The description adds minimal extra meaning beyond 'optional filters narrow by category, difficulty, or maximum duration.' Most value comes from the schema itself.
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 searches the tutorial corpus by free-text query across titles, descriptions, and step content, and returns slim summaries with TL;DRs and canonical URLs. This distinguishes it from sibling tools like list_tutorials_by_category (category-only filter) and get_tutorial (single tutorial fetch).
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 when to use this tool (free-text search) vs. siblings (list by category, recent, etc.), but does not explicitly state when not to use it or provide alternative names. It mentions tenant-awareness, which is important context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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