Sourced answers to everyday "why is my…" questions — health, pets, home, plumbing, tech, cars.
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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.
Average 4.3/5 across 3 of 3 tools scored.
Each tool has a distinct purpose: listing topics, searching articles, and retrieving a specific article. No overlapping functionality.
All tool names follow a consistent verb_noun snake_case pattern (list_topics, search_articles, get_article) with clear, predictable naming.
Three tools are well-scoped for a knowledge base server: browse topics, search articles, and get a specific article. This is a focused set that covers the core workflow without bloat.
The tools cover the main use case of discovering and retrieving articles, but get_article only returns a summary, requiring users to visit the URL for full content. This minor gap slightly limits completeness.
get_articleRead a WhyIsMy.org articleAGet the direct answer, the causes the article covers, and its FAQ for one article, by slug (from search_articles). Returns a summary, not the full text — the article URL has the complete step-by-step fix and the sources.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | No | Article slug, e.g. "why-is-my-phone-on-sos". | |
| query | No | Optional. If slug is unknown or wrong, the closest match to this text is returned instead. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds behavioral context: it returns a summary, not full text, and that the article URL contains the complete step-by-step fix and sources. No contradictions.
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 that are front-loaded and concise, with no unnecessary words. 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?
For a simple tool with two parameters and no output schema, the description fully explains what is returned and what is not. It also points to the article URL for more details, 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%, so baseline is 3. The description adds that the slug is from search_articles and that the query is a fallback, but does not significantly enhance the schema descriptions.
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 retrieves the direct answer, causes, and FAQ for a single article by slug. It distinguishes from siblings by specifying that it returns a summary, not the full text, and that the slug comes from 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 explicitly mentions that the slug should come from search_articles and that an optional query can be used if the slug is unknown. It does not directly state when not to use it, but the context is sufficient for an agent to decide.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_topicsList WhyIsMy.org topicsAList the subject areas covered on WhyIsMy.org with an article count for each. Takes no arguments. Use the returned slugs as the topic argument to search_articles.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations (readOnlyHint, idempotentHint, destructiveHint) already cover safety and idempotency. Description adds no extra behavioral traits beyond stating 'Takes no arguments', which aligns with annotations. No contradiction.
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 purpose, no filler. 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?
Without an output schema, description sufficiently describes return: subject areas with article count and slugs. Could mention sorting or more detail on output structure, but adequate for a simple list tool.
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?
No parameters exist, so description correctly states 'Takes no arguments'. Baseline 4 applies as schema coverage is 100% and description adds clarity on absence of parameters.
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 'list' and resource 'subject areas covered on WhyIsMy.org with an article count for each'. It distinguishes from siblings by focusing on topic listing rather than article retrieval.
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 says 'Takes no arguments' and advises to use returned slugs as argument to search_articles. Provides clear post-use guidance, though no when-not-to-use is needed for a simple list tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_articlesSearch WhyIsMy.org articlesAFind WhyIsMy.org articles answering a "why is my…" question. Returns titles, summaries and URLs. Both arguments are optional: omit query to browse, pass topic to bias results toward one subject area. The returned summaries are partial — send the user to the article URL for the full diagnosis, fixes and cited sources.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results, 1-25. Default 5. | |
| query | No | What the user is asking, e.g. "phone stuck on SOS" or "why is my dog shaking". | |
| topic | No | Optional topic slug to bias results (see list_topics), e.g. "health", "dogs", "plumbing". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds meaningful context: returns partial summaries and advises directing users to the URL for full details, which is critical for proper agent guidance.
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 covering purpose, output, parameter usage, and an important behavioral note (partial summaries). It is front-loaded with the key verb and resource, with no extraneous 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 has three optional parameters, no output schema, and comprehensive annotations, the description covers all aspects: what it returns, how to use parameters, and the nature of the output (partial summaries). It also suggests a follow-up action (send user to URL), ensuring completeness for agent decision-making.
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?
With 100% schema coverage, parameters are well-documented in the schema. The description adds value by explaining usage patterns (omit query to browse, topic biases results) that go beyond purely technical definitions.
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 finds articles answering a 'why is my' question and specifies it returns titles, summaries, and URLs. It distinctly distinguishes itself from siblings (get_article for full content, list_topics for topic slugs) by focusing on search and browse functionality.
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 explains both optional arguments, including the ability to omit query to browse and pass topic to bias results. It implies not to use for retrieving full article content, suggesting the user visit the URL or use get_article instead, though explicit sibling comparisons are not stated.
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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{
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