raumnebenan
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raumnebenan.de is a resource hub focused on actionable product thinking for product owners, product designers, business analysts, product managers, agile coaches, and user researchers. The site provides clear, structured guides and tools to help teams understand users, identify opportunities, validate ideas, and deliver products that create real user and business value.
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Tool Definition Quality
Average 4.1/5 across 8 of 8 tools scored. Lowest: 3.2/5.
Each tool has a clearly distinct purpose: listing categories, stories, articles; retrieving articles by slug, UUID, or tag; searching articles; and fetching stories by category. No two tools have overlapping functionality.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., list_articles, get_article_details_by_slugs, search_articles). The naming is predictable and uniform.
With 8 tools, the set is well-scoped for a knowledge base server. Each tool serves a necessary function for browsing and retrieving content without being excessive or insufficient.
The tool set covers listing and retrieval of all content types (categories, stories, articles) via multiple identifiers. A minor gap is the lack of a direct way to get articles by category, though stories by category and articles by tag partially compensate.
Available Tools
8 toolsget_article_details_by_slugsAInspect
Returns a list of published articles by their exact slugs (slugs), for example: kano-model, jobs-to-be-done, from the provided raumnebenan source. Use this when you already know the exact article slugs from the Reader structure (pillar -> story -> article). If the slugs are unknown, use search_articles first and then get_articles_by_uuids. Use only this tool output; do not use external or inferred data. If required information is missing in this source, respond that it is not available in the provided source. Only JSON-RPC 2.0 requests are supported.
| Name | Required | Description | Default |
|---|---|---|---|
| article_slugs | Yes | A list of article slugs |
Output Schema
| Name | Required | Description |
|---|---|---|
| articles | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description fully covers behavioral aspects. It states the output is a list of published articles from the specified source, requires exact slugs, and instructs to not use external data. The description also clarifies handling missing information.
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 (a few sentences) and front-loaded with the primary action. Information is well-structured, covering what the tool does, when to use, and constraints without unnecessary 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 simple parameter set and the presence of an output schema, the description provides sufficient context. It covers usage scenarios, constraints, and fallback behavior, making it complete for an agent to understand the tool's 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 coverage is 100% with descriptions for the single parameter. The description adds value by emphasizing exact slug matching, providing examples, and connecting the parameter to the Reader structure, which goes beyond the schema alone.
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 the tool returns a list of published articles by exact slugs, provides examples (kano-model, jobs-to-be-done), and specifies the source (raumnebenan). It distinguishes from sibling tools by referencing the Reader structure (pillar -> story -> article) and mentions alternative tools for different scenarios.
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 clearly states when to use this tool (when exact slugs are known from the Reader structure) and when not to (if slugs unknown, use search_articles then get_articles_by_uuids). It also includes guidance to not use external or inferred data and to respond if information is missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_article_details_by_uuidsAInspect
Returns a list of published articles. Use this when you already know the exact article uuids after you have invoked the tool list_articles. If the uuid is unknown, you can additionally use search_articles first. Important: Use only this tool output in your response and do not use external or inferred data. If required information is missing in this source, respond that it is not available in the provided source. Only JSON-RPC 2.0 requests are supported with this tool.
| Name | Required | Description | Default |
|---|---|---|---|
| article_uuids | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| articles | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must compensate. It mentions JSON-RPC 2.0 support and restriction to its output, but does not disclose what happens for invalid UUIDs, rate limits, or data freshness. Adequate but not thorough.
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?
Four sentences, front-loaded with purpose, then usage context and warnings. Could be slightly more concise by merging the warning about external data with the usage instruction, but overall efficient.
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 1 required parameter, an output schema (exists but not shown), and sibling tools, the description provides the necessary workflow context (list_articles then this tool) and protocol constraint. Minor gap: no mention of pagination or empty results.
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 parameters have no descriptions. The description only says 'article uuids' but does not explain the format (e.g., what a valid UUID looks like) or provide additional context beyond the schema property name.
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 published articles given exact UUIDs. It distinguishes from siblings like search_articles and get_article_details_by_slugs by specifying the prerequisite of knowing UUIDs from list_articles.
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?
Provides explicit when-to-use (after list_articles) and when-to-use alternatives (search_articles if UUID unknown). Also instructs to rely only on this output and not external data.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_articles_by_tagBInspect
Returns all published articles that include the specified tag. Provide a single tag string as input. The response contains all articles where this tag is present in their tags list.
| Name | Required | Description | Default |
|---|---|---|---|
| tag | Yes | The tag to fetch articles for. |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It only states the function without revealing safety traits (read-only vs destructive), pagination behavior, what happens on missing tag, or any permissions/rate limits. The description is minimal and does not add value beyond the basic 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?
The description is concise (two sentences) and front-loaded with the main purpose. It efficiently conveys the operation without unnecessary words. However, it could be slightly more structured (e.g., separating input and output behavior explicitly).
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 output schema exists, the description does not need to detail return values. However, it lacks information about empty results, tag case sensitivity, or pagination. For a simple list tool with one parameter, this is minimally adequate but not 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?
Schema coverage is 100% and the parameter 'tag' is well-documented in the schema. The description repeats 'Provide a single tag string as input' which adds marginal clarity but does not go beyond what the schema already provides. 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: return all published articles that include a specific tag. It uses a specific verb-resource combination ('returns articles') and distinguishes itself from siblings like list_articles (all articles) or search_articles (general search) by focusing on tag-based filtering. No ambiguity.
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. It does not mention scenarios where other tools (e.g., search_articles for multiple criteria or list_articles for unfiltered list) would be more appropriate. The description only states what the tool does, not when to choose it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_stories_by_categoryAInspect
Returns all published stories for a given category (e.g., foundation, sense, focus, discovery, delivery). The input must be a single category string.
| Name | Required | Description | Default |
|---|---|---|---|
| category | Yes | The category to fetch stories for. Must be one of: foundation, sense, discover, focus, deliver. |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description alone must convey behavioral traits. It indicates a read operation but does not mention pagination, ordering, or potential limits, which are relevant for a tool returning 'all' published stories.
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 with two short sentences, front-loading the purpose and providing immediate clarity. 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 presence of an output schema and the simple nature of the tool (single parameter, enum input), the description is fairly complete. It could mention pagination or error handling but the output schema likely covers return values.
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 already provides full details (enum, description) for the single parameter. The description adds examples and reinforces constraints but does not significantly enhance understanding 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 tool returns published stories filtered by a given category, provides examples, and specifies that input must be a single category string. This distinguishes it from sibling tools like list_articles or search_articles.
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 category-based queries but does not explicitly compare to alternatives or state when to use this tool over others like get_articles_by_tag or list_stories.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_articlesAInspect
Returns all published articles from the raumnebenan source. The articles are organized into five pillars (= categories): Foundation, Sense, Focus, Discovery, and Delivery. Stories are thematic groups under these pillars and contain the article entries. When you are asked to list all articles then show a categorized list or table of articles grouped by their main pillar. Use only this tool output; do not use external or inferred data. If required information is missing in this source, respond that it is not available in the provided source. Only JSON-RPC 2.0 requests are supported.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| source | No | |
| stories | No | |
| copyright_holder | No | |
| source_friendly_url | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that only JSON-RPC 2.0 requests are supported and instructs to avoid external data. It also implies the tool returns all published content without pagination (since no parameters). However, given the absence of annotations, the description does not cover other behavioral aspects like authentication, rate limits, or data freshness. It provides moderate transparency but has room for improvement.
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 main purpose and includes necessary structural details. It is somewhat wordy with instructions about response formatting and supported requests, but each sentence adds value. Still, it could be slightly more concise.
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 absence of annotations and parameters, the description provides sufficient context about the tool's output (pillars, stories) and usage instructions. The mention of output schema existence suggests structured return, but the description itself fills gaps. It is complete enough for an agent to understand the tool's behavior.
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?
There are no parameters, so schema coverage is 100% by default. The description does not need to add parameter information. It instead provides valuable context about the output structure (pillars, stories), which aids in interpreting results. This compensates for the lack of parameter details.
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 that the tool returns all published articles from the raumnebenan source. It also explains the organizational structure (pillars and stories), which adds clarity. However, it does not explicitly differentiate from sibling tools like 'get_articles_by_tag' or 'search_articles', though the purpose is implicitly distinct by returning all articles.
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 advises to use this tool when asked to list all articles and to present results grouped by pillar. It provides clear context for its use. However, it does not mention when not to use it or explicitly contrast with alternatives, leaving some ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_categoriesAInspect
Returns a list of all available product knowledge categories, each with a short description. Categories represent the main pillars of Product Thinking – Foundation, Sense, Focus, Discovery, and Delivery. Each category provides structured resources for product owners, designers, and teams, covering groundwork, user research, opportunity analysis, validation, and agile delivery. Use this tool to guide users to the right area for their current product challenge.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses the return type and content (list with descriptions and specific categories), making the behavior predictable, though it could mention it's read-only.
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?
Three sentences: purpose, elaboration, and usage guidance. No wasted words, 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 simplicity of the tool (no parameters, output schema exists), the description sufficiently covers what it does, what it returns, and how to use it.
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 tool has zero parameters and 100% schema coverage, so the description adds no parameter semantics but meets the baseline for completeness.
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 all available product knowledge categories, with examples, distinguishing it from sibling tools that handle articles or stories.
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 includes a usage directive ('Use this tool to guide users to the right area for their current product challenge'), providing context but not explicitly comparing to alternatives or stating when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_storiesAInspect
Returns all published stories from the raumnebenan source. Only JSON-RPC 2.0 requests are supported.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| source | No | |
| stories | No | |
| copyright_holder | No | |
| source_friendly_url | No |
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 only states it returns published stories and the request format, but does not mention pagination, ordering, rate limits, or what happens when no stories exist. This is insufficient for a read 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?
The description is only two sentences long, each serving a clear purpose: stating what the tool does and specifying a request format constraint. No unnecessary information is present.
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 low complexity (no parameters, simple list), the description is minimally complete. However, it could mention that the output schema exists or clarify that no filtering is available. With an output schema present, the return values are covered, but behavioral details are missing.
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, and schema description coverage is 100%. Baseline for no parameters is 4, and the description adds no additional parameter information since none exist, which 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 returns 'all published stories from the raumnebenan source', specifying the verb, resource, and scope, distinguishing it from sibling tools like list_articles or get_stories_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?
The description mentions that only JSON-RPC 2.0 requests are supported, but does not provide guidance on when to use this tool versus siblings (e.g., list_articles returns articles, not stories). Usage context is implied 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.
search_articlesAInspect
Searches published article content from the provided raumnebenan source with case-insensitive text matching. Use this as the first discovery step when the exact slug or id is unknown, then call get_article_by_id for full details. Use only this tool output; do not use external or inferred data. If required information is missing in this source, respond that it is not available in the provided source. Only JSON-RPC 2.0 requests are supported.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The search query string |
Output Schema
| Name | Required | Description |
|---|---|---|
| articles | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but description reveals case-insensitive matching, source constraint, and JSON-RPC requirement. Could mention error behavior but output schema exists.
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?
Four sentences each add distinct value: purpose, usage guidance, output usage, protocol format. No unnecessary 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?
For a single-parameter search tool with output schema, description covers purpose, usage, constraints, and protocol. Meets all needs.
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 covers parameter name and type; description adds context like case-insensitive matching and source, enhancing understanding beyond 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?
Description clearly states it searches published articles with case-insensitive text matching, distinguishing it from sibling tools like get_article_by_id which require exact identifiers.
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 guides to use as first discovery step when exact slug/id unknown, then call get_article_by_id. Also instructs to use only this tool output.
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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