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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Average 4.2/5 across 8 of 8 tools scored. Lowest: 3.6/5.
Each tool has a clear, distinct purpose: list tools for enumeration, get tools for retrieval by specific identifiers or filters, and search for free-text discovery. The two get_article_details variants are clearly differentiated by slug vs. UUID usage, and get_articles_by_tag vs. search_articles are distinct in their input criteria.
The naming pattern is predominantly consistent with list_* for all-resource retrieval, get_*_by_* for filtered access, and search_* for discovery. Minor deviations include the verbose 'get_article_details_by_slugs' vs. the simpler 'get_articles_by_tag', and a description in search_articles referring to a non-existent 'get_article_by_id' tool.
Eight tools provide a well-scoped surface for a read-only content API covering categories, stories, and articles. The count is neither too sparse nor overwhelming, and each tool contributes a unique retrieval mode.
The tool set covers the essential read operations: listing all entities, fetching by identifiers, filtering by tag or category, and text search. Minor gaps include no direct story-by-slug retrieval and no search across stories, but these are workable via existing tools.
get_article_details_by_slugsAInspectReturns a list of published articles by their exact slugs slugs for example overview 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 |
| Name | Required | Description |
|---|---|---|
| articles | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It discloses that only published articles are returned, requires exact slugs, specifies the data source, instructs how to respond when information is missing, and notes that only JSON RPC 2.0 requests are supported. This goes beyond minimal disclosure, though it doesn't mention pagination or error specifics.
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 somewhat wordy and contains a typo ('slugs slugs') and an awkward phrase. It front-loads the main purpose but then mixes usage guidance, source constraints, and protocol in a way that could be more organized. Not concise enough for a top score.
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 tool with an output schema, the description covers the essential context: function, usage, alternatives, source-specific behavior, and protocol. The main gap is clarity of the source phrase, but overall it is sufficiently 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 description coverage is 100%, so the schema already explains the parameter. The description adds emphasis on 'exact' slugs and gives an example, but no additional semantic detail beyond that. This aligns with the baseline of 3 for high schema 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 states the tool returns published articles by exact slugs, which is a specific verb+resource. It also distinguishes itself from siblings by noting to use it only when slugs are already known. However, the phrase 'from the Reader structure pillar story article' is confusing and the typo 'slugs slugs' detracts from clarity.
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 usage guidance: use when you know the exact slugs, and if unknown, use search_articles first then get_articles_by_uuids. It also includes a constraint to only use this tool's output and not external or inferred data, which is clear and actionable.
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_uuidsAInspectReturns 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 |
| Name | Required | Description |
|---|---|---|
| articles | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that only published articles are returned, includes an important instruction about relying solely on this tool's output and responding when information is missing, and notes JSON-RPC 2.0 support. It lacks details on error behavior or permissions, but these are less critical for a read-only retrieval 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 compact but information-dense, front-loading the purpose and providing necessary usage and constraints in four sentences. Each sentence serves a distinct function, though the JSON-RPC note is a minor implementation detail that could be omitted without losing core 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?
Given the tool's simplicity and the existence of an output schema, the description covers the purpose, usage prerequisites, and a data-fidelity rule. It is complete enough for an agent to select and invoke the tool correctly, with no major gaps in essential context.
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?
At 0% schema description coverage, the description compensates by explaining that article_uuids are the exact UUIDs obtained from list_articles, adding meaningful context beyond the schema's minimal 'An article UUID' item description. It clarifies the expected source and nature of the parameter values.
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 opens with 'Returns a list of published articles,' a specific verb+resource statement. It also clarifies the input context (exact article uuids) and distinguishes the tool from siblings like list_articles and search_articles by specifying when it applies.
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 'Use this when you already know the exact article uuids after you have invoked the tool list_articles' and gives an alternative: 'If the uuid is unknown, you can additionally use search_articles first.' This clearly communicates when to use this tool versus alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_articles_by_tagAInspectReturns 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. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the burden. It clearly indicates a read operation ('returns') and specifies the scope ('published articles'). However, it does not disclose potential pagination, error handling, or rate-limits, which could be relevant for a tool returning 'all' articles.
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, front-loaded with the primary purpose, no redundant phrasing.
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 one-parameter tool and presence of an output schema, the description covers the core behavior. It could mention edge cases like empty tag or pagination, but these are not critical for basic 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?
The schema provides 100% coverage for the single tag parameter. The description adds useful context that the tag should be a single string and that matching is by presence in the article's tags list, going slightly beyond the schema's basic description.
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 the tool returns all published articles filtered by a specific tag, which is a clear verb+resource+scope. It distinguishes from sibling tools that fetch details by slugs/UUIDs, search, or list 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?
It clearly indicates the use case: retrieving articles by tag. However, it does not explicitly mention when not to use it or point to alternatives, so it lacks explicit exclusion guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_stories_by_categoryAInspectReturns 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. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavioral traits. It states 'published' as a filter and implies a read-only operation, but does not disclose pagination, response size limits, or error behavior for invalid categories. It adds some value but leaves notable gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the primary action, and every word serves a purpose. It is highly concise with 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?
Given the tool's simplicity, the output schema presence, and full schema coverage, the description covers the essential information. It could be slightly more complete by not naming the input and clarifying the exact enum values, but overall it is sufficient for an agent to invoke the 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?
The schema provides 100% coverage of the category parameter with an enum and description, so the baseline is 3. The description adds examples and emphasizes 'single category string,' but those examples are slightly inaccurate relative to the schema, and no additional meaning is provided beyond what the schema already contains.
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 for a given category, using the verb 'returns' with a specific resource and filter. However, the example categories listed ('discovery', 'delivery') do not exactly match the enum values in the schema ('discover', 'deliver'), introducing minor confusion. It is still distinguishable from siblings like list_stories or get_articles_by_tag.
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 stories by category and states the input must be a single category string, but it does not explicitly mention when to use this tool versus alternatives like list_stories or search_articles. There is no exclusion guidance or when-not-to-use information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_articlesAInspectReturns all published articles. The content is organised in five Categories. Each category has multiple stories. Each story has multiple articles The five categories are Foundation, Sense, Focus, Discovery, and Delivery. Stories are thematic groups under these categories and contain the article entries. When you are asked to list all articles then provide a table of all articles showing as extra column the categories and stories that the reader can understand where the article belongs to. Use only output by httpswww.raumnebenan.demcp do not use other external or inferred data. If required information is missing in this source, respond that the required data is not available in the provided source. Only JSON-RPC 2.0 requests are supported.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| source | No | |
| stories | No | |
| copyright_holder | No | |
| source_friendly_url | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden. It discloses the data source restriction ('Use only output by...'), how to handle missing data, and protocol support ('Only JSON-RPC 2.0 requests are supported'). It also describes the expected output format (table with categories/stories), but does not cover auth, rate limits, or error behaviors.
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 somewhat verbose and has formatting issues (missing period, malformed URL). The purpose is front-loaded, and the category explanation is relevant, but redundancy in explaining the hierarchy could be tightened.
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?
Despite the verbosity, the description provides all necessary context: what is returned, the organizational structure, presentation requirements, and data source constraints. The presence of an output schema reduces the need to describe return values, making this fairly complete. Minor gaps include no mention of sorting or error handling, but for a simple list tool this is acceptable.
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?
This tool has 0 parameters, so the baseline is 4. The description adds no parameter info, which is appropriate since the schema is empty and coverage is 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?
The description opens with 'Returns all published articles', a specific verb and resource. It further clarifies the article organization and distinguishes itself from sibling tools that filter or search by explicitly stating it returns 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?
It explicitly states 'When you are asked to list all articles' and instructs to produce a table, giving clear context. However, it does not mention alternatives or when not to use this tool, earning a 4 rather than a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_categoriesAInspectReturns 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 | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It describes the return value (list of categories with descriptions) and explains the category taxonomy, which is useful context. Though it does not explicitly state read-only or mention side effects, 'returns a list' implies a safe 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 three sentences, front-loaded with the main action, and each sentence adds meaningful context: what it returns, category examples, and usage guidance. No redundant language.
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, zero-parameter tool with an output schema, the description is complete. It explains the tool's purpose, content, and usage context, and does not need to detail return structure since the output schema exists.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the baseline is 4. The description adds value by explaining what the categories are and their purpose, which helps the agent understand the output, though there is no parameter-specific info needed.
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 the tool returns a list of product knowledge categories with short descriptions, using the specific verb 'returns' and resource 'categories'. It distinguishes from siblings by focusing on the category hierarchy rather than article/story 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?
Provides clear context for when to use the tool: to guide users to the right area for their product challenge. Does not explicitly list alternatives or when-not-to-use, but sibling tools cover different resources, making the intended use obvious.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_storiesAInspectReturns all published stories from the raumnebenan source. Only JSON-RPC 2.0 requests are supported.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| source | No | |
| stories | No | |
| copyright_holder | No | |
| source_friendly_url | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses a protocol constraint (JSON-RPC 2.0 only) and the published-status filter, but omits potential behavioral details like pagination, ordering, or error handling. For a simple list tool, this is moderate 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 two sentences: the first states the core function, the second adds a necessary protocol constraint. No wasted words and the purpose is 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?
The tool has no parameters and an output schema exists, so the description covers the essential purpose and a key constraint. It lacks explicit differentiation from siblings, but for a simple list operation it is largely 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?
The tool has zero parameters, so the schema provides no parameter semantics. Per rubric, a zero-parameter tool gets a baseline of 4, and the description adds no additional parameter information because none exists.
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, using the specific verb 'Returns' and identifying the resource scope. It distinguishes from siblings like list_articles (different resource) and get_stories_by_category (filtered variant).
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 explicit guidance on when to use this tool over alternatives such as get_stories_by_category or list_articles. The purpose statement implies use when all published stories are needed, but no exclusions or alternative recommendations are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_articlesAInspectSearches 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 |
| Name | Required | Description |
|---|---|---|
| articles | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does so thoroughly. It discloses case-insensitive matching, the specific data source (raumnebenan), a directive to rely only on this tool's output and not infer data, and a note that only JSON RPC 2.0 requests are supported. These are meaningful behavioral traits beyond the schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by usage guidance and specific constraints. It is slightly longer than necessary but every sentence adds critical operational information such as data provenance and JSON RPC support, so there is little waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter search tool with an output schema, the description is complete. It covers purpose, when to use it, how to handle missing information, and technical constraints. The output schema handles return values, so nothing essential is 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 schema describes 'query' as 'The search query string,' which is brief. The description adds behavioral context: the query is used for case-insensitive text matching, clarifying how the parameter is interpreted. While schema coverage is 100%, this additional semantic detail elevates it above the baseline of 3.
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 opens with a specific verb and resource: 'Searches published article content from the provided raumnebenan source with case insensitive text matching.' It clearly distinguishes from sibling tools by framing this as the discovery search for unknown slugs or ids, contrasting with detail-fetching tools like get_article_by_id.
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?
Explicit usage guidance is provided: 'Use this as the first discovery step when the exact slug or id is unknown then call get_article_by_id for full details.' This states when to use it, gives a clear workflow, and references an alternative for the next step.
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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