atlaxy

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must carry the burden of disclosing behavior. It does not explain what the four scoring dimensions are, whether the operation is read-only, what output format is returned, or how the comparison is structured. It only restates the basic action.

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 with no filler, and the core action and key constraint are front-loaded. It is concise and well structured for the limited information it conveys.

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?

With no output schema and no annotations, the description is the sole source of context. It omits the meaning of the four scoring dimensions, the return value, and practical limitations beyond the count, leaving an agent under-informed for confident 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?

Schema description coverage is 0%, and the description does not explicitly define what 'slugs' refers to beyond implying they identify the entities to compare. It adds the 2–5 range constraint but omits details such as ordering, uniqueness, or entity type requirements.

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 a specific verb ('compare') and resource ('entities'), with a clear count range and the object of comparison ('four scoring dimensions'). It differentiates from sibling tools that retrieve, list, or search single entities, though the scoring dimensions themselves are not named.

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 is provided on when to use this tool versus get_entity, search_entities, or list_entities. The count range implies multi-entity comparison, but there is no explicit context, prerequisites, or exclusion criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It communicates a read-style operation and lists record contents, but it does not disclose potential errors, authentication requirements, response shape details, pagination behavior, or any limitations. This is a notable gap for a tool with no annotation support.

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, tightly written sentence that front-loads the core action and resource, then appends a useful list of return contents. Every word contributes meaning, with no filler or repetition.

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 one-parameter read tool with no output schema, the description covers both the input requirement and the expected return contents. It does not describe error cases or response format, but the enumerated record components give an agent enough context to invoke the tool and interpret the result.

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 only a string type for 'slug' with no description, while schema description coverage is 0%. The description compensates by explicitly stating that the slug identifies the entity being retrieved. For a single-parameter tool, this is sufficient guidance, though it could have added format details or clarified what constitutes a valid slug.

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 a specific action ('Get the full record for one entity'), the lookup key ('by slug'), and enumerates the contents of the record (scores, evidence, sources, tags, links). This clearly distinguishes it from sibling tools like list_entities or search_entities, which involve multiple entities or 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 appropriate use case: when the agent has a slug and needs a single entity's full record. However, it does not explicitly state when NOT to use it or call out alternatives such as list_entities for browsing or get_entity_relations for relationship data.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. The verb 'get' signals read-only, but the description does not state auth requirements, rate limits, pagination, or side effects. It also doesn't specify whether the result is a list or how confidence relates to filtering, leaving behavioral expectations vague.

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?

A single sentence that front-loads the core action and resource, with examples and return fields. There is no filler or redundancy, and the ellipsis keeps it compact while indicating additional relation types.

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?

With no output schema and no annotations, the description should specify what a caller receives and any constraints. It provides the return payload's two key attributes (confidence and target names), but it omits the list shape, pagination logic implied by limit, and the meaning of relation types. For a straightforward read tool this is adequate, but with three parameters and no schema descriptions it leaves gaps.

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 the description must compensate. It mentions 'confidence,' which hints at the meaning of min_confidence, and 'target names' hints at output, but slug and limit are not explained beyond their names. The description adds only marginal semantic value for the 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 uses a precise verb ('get') and a specific resource ('an entity's relations'), enumerates relation types (alternatives, built-on, integrations) and states two return fields (confidence, target names). This distinguishes it from siblings like get_entity, which returns the entity itself, and list_entities, which returns a collection of entities.

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 this tool is for retrieving relation data for a single entity, but it never states when to prefer it over compare_entities or search_entities, nor does it mention any exclusions or alternatives. The phrase 'entity's relations' provides some contextual guidance, but there is no explicit when/when-not guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the behavioral disclosure burden. It clearly indicates a read-only retrieval by using 'Get' and names the metadata categories returned, but it does not describe response format, error behavior, or whether the 'current' snapshot can change between calls.

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 with no filler words, and the core action and resource appear first. The enumeration of version, counts, freshness, and validation efficiently conveys the tool's scope.

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 zero-parameter, read-only metadata getter with no output schema, the description provides a reasonable overview of the returned information. It could be slightly more explicit about formatting or how 'current' and 'published' are determined, but overall it is sufficient for an agent to understand the tool's role.

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 the schema already provides complete coverage, so the standard baseline of 4 applies. The description adds contextual meaning by clarifying what kind of metadata will be returned, even though no parameter-specific semantics are 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?

The description identifies a specific verb ('Get'), a specific resource ('current published snapshot metadata'), and enumerates the key information returned. None of the sibling tools target snapshot metadata, so it is clearly distinguished from the alternatives.

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 tool should be used when the current published snapshot metadata is needed, but it does not explicitly state when to use it versus alternatives or mention any exclusion conditions. Since no sibling tool overlaps with snapshot metadata, the lack of explicit exclusions is a minor gap.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states that the entire taxonomy is retrieved, but does not describe the output shape, whether the data is hierarchical, or whether this is a cached/static dataset. The read-only nature is implied by 'Get' but not explicitly disclosed.

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 short sentence that conveys the essential operation and scope with no filler. Every word earns its place and the key resource 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?

For a zero-parameter read-only lookup, the description is minimally sufficient: an agent knows what to call and that no arguments are required. However, because there is no output schema and no annotation, the description leaves the return structure and format of the taxonomy unspecified.

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 takes zero parameters and schema description coverage is 100%, so there are no parameter semantics for the description to clarify. Baseline for a zero-parameter tool is 4, and the description adequately signals that no inputs are 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?

The description uses a specific verb ('Get') and a specific resource ('full continent + region taxonomy'), clearly distinguishing it from the entity-, snapshot-, track-, and search-focused siblings. There is no ambiguity about what the tool returns.

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 is given about when to use this tool versus the alternatives, nor are any exclusions provided. While the resource name implies its purpose, the description does not actively help an agent choose between this and sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the behavioral burden. It communicates that this is a read-only listing operation with optional filtering and score-based ranking. However, it does not disclose the return shape, pagination behavior, or what happens when score_mode is omitted, so transparency is only partial.

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?

A single concise sentence that front-loads the core operation and resource, then packs the key options into a compact phrase. No wasted words or redundant details.

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 four parameters, no annotations, and no output schema, the description leaves important gaps: it doesn't explain the return format, default limit behavior, or how this tool relates to search_entities/list_tracks. An agent could call it correctly in the obvious case but would lack information needed for confident invocation across scenarios.

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%, but the description adds meaning by mapping continent and entity_type to 'filtered by' and score_mode to 'ranked by'. It leaves limit unmentioned and doesn't explain enum value semantics or how filters combine, so the compensation is incomplete.

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 names the operation ('List') and resource ('entities') and adds useful detail about optional filters and ranking by score_mode. However, it doesn't explicitly differentiate this from sibling tools like search_entities or list_tracks, leaving some inference required.

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?

There is no guidance on when to use this tool versus alternatives such as search_entities or get_entity. The description states only what the tool does, not the context or preconditions that should lead an agent to select it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the behavioral disclosure burden. It does disclose the output contents (node counts and top nodes) and the fixed count (12), but it does not mention return format, ordering, potential errors, or whether the operation is read-only, though 'List' strongly implies 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 a single well-structured sentence that front-loads the action and resource, then adds the key output details. Every word contributes meaning, and there is no redundant or vague 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?

For a zero-parameter list tool, the description is nearly complete: it names the resource, the expected count, and the included data. It does not specify the exact response structure, but the absence of an output schema makes the description's high-level outline acceptable 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 tool has zero parameters, so parameter descriptions are not needed. The description's focus on output contents is sufficient; no parameter semantics are required for an agent to invoke this tool correctly.

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 a specific resource ('AI continents (tracks)'), and it states exactly what is returned: node counts and top nodes. This clearly distinguishes it from sibling tools like list_entities, which presumably lists entities rather than tracks.

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 this tool is for retrieving the fixed set of 12 tracks, but it does not explicitly state when to choose it over alternatives or when not to use it. No exclusions or sibling comparisons are given, leaving usage context largely implicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are present, so the description must carry the behavioral burden. It does add useful disclosure: results are ranked and include scores and match reasons. However, it doesn't cover pagination, scoring semantics, or any side effects, which is a moderate gap for a 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?

Two dense sentences with no filler—action, resource, and result are front-loaded. It earns each word without restating schema fields.

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 6-parameter search tool with no annotations and no output schema, the description is underspecified. It explains the core query and return shape but omits parameter semantics and edge cases, making full utilization difficult.

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?

Only q and continent have schema descriptions; tag, type, limit, and score_mode are largely undocumented, and the tool description does not compensate. The phrase 'ranked matches with scores' hints at score_mode but doesn't explain its enum values or the limit/tag/type filter behaviors. Coverage is only 33%, so agents would have to guess at most 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 identifies a specific action (search by keyword), target resources (AI products, models, agents, organizations), and return value (ranked matches with scores and why they matched). This separates it from siblings like get_entity and list_entities, though 'agents' doesn't map to the type enum's values.

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 tool is for keyword-driven discovery, but it doesn't state when to prefer search_entities over list_entities or get_entity, nor mention any exclusions. There is no explicit when/when-not guidance, leaving the agent to infer use cases from the tool name.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.