Machine-readable taxonomy of 100+ AI system failure modes spanning factuality, alignment, planning, code generation, and instruction following.
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Average 4.1/5 across 4 of 4 tools scored.
Each tool serves a distinct purpose: retrieving a single failure mode, listing categories, listing modes with optional filtering, and searching by keyword. There is no overlap or ambiguity between them.
All tool names follow a consistent verb_noun pattern using snake_case: get_failure_mode, list_categories, list_modes, search_failure_modes. The verb clearly indicates the action, and nouns are descriptive.
With only 4 tools, the set is tightly scoped for a read-only taxonomy querying server. Each tool earns its place, covering retrieval, listing, filtering, and search without unnecessary bloat.
The surface covers the core read operations needed for a taxonomy: single record retrieval, categorical listing, and text search. Only minor gaps exist, such as no direct 'get category' tool, but list_categories suffices.
get_failure_modeAInspectRetrieve the full structured record for a single failure mode by its ID slug (e.g. 'citation-hallucination', 'sycophancy').
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | The failure mode ID slug, e.g. 'citation-hallucination' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of behavioral disclosure. It accurately identifies the operation as read-only retrieval and mentions that the output is a 'full structured record', but it does not discuss error handling (e.g., invalid ID), permissions, or the exact return format. This is adequate for a simple getter but lacks depth.
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 concise sentence that front-loads the core purpose and includes a clarifying example. No filler words or repetitive 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 one-parameter get-by-ID tool, the description is sufficiently complete: it names the parameter, gives examples, and indicates the return type. While no output schema is present, the tool's simplicity and the 'full structured record' phrase make the description adequate without further elaboration.
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%, and the schema already provides the same example ('citation-hallucination'). The description reinforces the ID slug concept but does not add new parameter-level semantics beyond what the schema already states, so the 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 a specific verb ('Retrieve') and resource ('full structured record for a single failure mode'), with the retrieval method ('by its ID slug'). This distinguishes it from sibling tools like list_modes or search_failure_modes, which handle collections or searches.
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 the tool: when you already have a failure mode ID slug. It provides concrete examples, but does not explicitly contrast with sibling tools or state when not to use it. Still, the context is clear and unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_categoriesAInspectList all taxonomy categories with their IDs, names, descriptions, and mode counts.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It transparently states the tool returns all categories and lists the exact fields included, which is sufficient for a simple read-only listing. It does not discuss permissions, pagination, or side effects, but for this straightforward operation the description is informative.
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, front-loaded sentence that states the action and output fields without any padding. Every word contributes to the meaning.
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 and no annotations, the description provides the essential return fields (IDs, names, descriptions, mode counts). It is complete enough for a simple list-all tool, though it omits potential details like pagination or ordering, keeping it from a perfect score.
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 the description adds no parameter-related details. Per the rubric, a baseline of 4 is appropriate when there are no parameters, since there is nothing to explain.
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 lists all taxonomy categories and specifies the returned fields (IDs, names, descriptions, mode counts). This specific verb+resource combination distinguishes it from sibling tools like list_modes and search_failure_modes.
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 is given on when to use this tool versus alternatives. The context is implied by the name and description—use this to list categories rather than modes or failure modes—but no alternatives or exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_modesAInspectList failure modes, optionally filtered to a single category by category ID.
| Name | Required | Description | Default |
|---|---|---|---|
| category | No | Optional category ID to filter by, e.g. 'factuality'. Omit to list all modes. |
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 discloses the tool's core behavior (listing and optional filtering) but does not mention return format, ordering, pagination, or any side effects. For a simple read-only list, this is adequate but not rich.
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 verb and resource, then adds the optional filter. There is zero redundancy and every word contributes to understanding the tool's functionality.
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 single-parameter tool, the description plus schema fully specify what it does and how to invoke it. Without an output schema, it could briefly mention the return structure, but 'List failure modes' sufficiently implies a list. It lacks explicit mention of related tools, but this is not essential for correct 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 input schema already provides 100% coverage: the 'category' parameter includes a clear description and an example ('factuality'), plus behavior on omission. The description's mention of category ID adds no additional semantic value, so the baseline of 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 uses a specific verb ('List') and resource ('failure modes'), and clarifies optional filtering by category ID. This clearly distinguishes it from siblings like get_failure_mode (single item), list_categories (different resource), and search_failure_modes (search behavior).
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 the primary use case (listing modes, optionally filtered) and the 'omit to list all modes' guidance clarifies the parameter behavior. However, it does not explicitly mention when to prefer this over sibling search_failure_modes or get_failure_mode, so it lacks explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_failure_modesAInspectSearch the Fail Modes taxonomy by symptom text or keyword phrase. Returns ranked matches with id, name, category, and matched context.
| Name | Required | Description | Default |
|---|---|---|---|
| symptom | Yes | Symptom phrase or keyword to search for, e.g. 'made up a citation' or 'refused to help after pushback' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It discloses that results are ranked and includes the return schema (id, name, category, matched context), giving the agent a clear behavioral picture. It does not mention side effects, but 'search' implies read-only and the provided details are sufficient for a simple search 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 verb+resource and includes essential output details. No wasted words, and it is well-structured for quick parsing.
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 is simple with one parameter and no output schema, and the description adequately explains the return fields. It lacks details like result limits or pagination, but for a search tool with ranked results, the provided information is reasonably 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% and includes examples, so the description adds minimal extra meaning. It reinforces the 'symptom' parameter's role but does not go beyond the schema's detailed explanation. 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?
The description clearly states the tool searches the Fail Modes taxonomy by symptom text or keyword phrase, with a specific verb and resource. It distinguishes from siblings (get/list) by focusing on text-based search, and specifies the return fields.
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 when the user has a symptom phrase or keyword to look up, which is clear context. It does not explicitly mention alternatives or exclusions, but the search-oriented purpose effectively guides when to use it versus get/list tools.
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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