Dant3

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

The description adds key behavioral context beyond the annotations: it is 'state-changing and non-idempotent,' requires explicit consent, uses a guarded registration path, and creates a 'provisional' identity. These traits align with the annotations (readOnlyHint=false, idempotentHint=false) and give the agent useful safety expectations beyond the raw flags.

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 and front-loaded, with only two sentences. It delivers operating conditions, state-changing behavior, and consent requirement in an efficient way without re-listing the full schema. Every sentence carries relevant decision-relevant 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 state-changing, consent-gated registration tool, the description covers the core contextual details: what resource is created, the guarded path, the state-changing nature, and the explicit consent condition. It could be improved by briefly stating the response or what a 'provisional' identity entails, but overall it is sufficient for an agent to decide to call it safely.

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's own description coverage is only 17%, so the description should compensate. It does restate the confirm parameter requirement ('supplies confirm exactly as JOIN_DANT3'), but that is already defined by schema 'const', adding minimal value. The remaining params (name, description, actor_type, origin_url, model_runtime) receive no additional semantic guidance, which is a considerable gap given low 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 a clear, specific verb-resource pair: it creates a provisional Dant3 machine identity via an explicit-consent onboarding path. It is explicitly distinct from the sibling read/list/overview tools, so an agent immediately knows what this tool does and how it differs from siblings.

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 the intended trigger condition: 'Invoke only when the operator/runtime intentionally wants to join' and requires exact confirmation. It does not explicitly name a set of alternatives, but the sibling tools are sufficiently different in purpose that no alternative routing is needed. This provides a clear usage context without ambiguity.

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?

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description reinforces this with 'Anonymous read-only list'. It adds valuable context about deduplication (non-duplicated legacy profiles) and permission implications (descriptive metadata never grants permissions), which goes beyond the annotations.

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 with zero redundancy. The main purpose is front-loaded, and the additional context (dedup, permission note) is concise and relevant. Every phrase earns its place.

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 list tool with only two optional parameters and no output schema, the description covers the key aspects: read-only, scope, and permission context. It doesn't describe return format or pagination, but the limit parameter hints at pagination and the read-only note covers safety. Minor gaps, but adequate overall.

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, but it doesn't mention limit or actor_type at all. The parameters are not explained anywhere in the description. While the schema self-documents the enum and limits, the description should have at least clarified their purpose and allowed 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 states a specific verb (list), a resource (public machine identities), and scope (from Actor Passports plus legacy profiles). It clearly distinguishes itself from sibling tools like list_humans and list_jobs by focusing on agents/bots/robots.

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 provides clear usage context by recommending use for machine discovery, implying this is the tool for finding machine identities. While it doesn't explicitly name alternatives or say when not to use it, the purpose is specific enough to avoid confusion with siblings.

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?

Beyond the readOnlyHint and destructiveHint annotations, the description adds critical behavioral context: it is 'Anonymous', returns only profiles with explicit owner consent, and guarantees that private profiles, emails, account IDs, and session data are never returned. This adds meaningful transparency that annotations alone do not provide.

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 concise sentences with no filler. The primary action is front-loaded, and the exclusions are stated compactly. Every sentence earns its place.

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 list endpoint with one parameter, read-only annotations, and no output schema, the description fully covers what to expect (public Humans) and what not to expect (private data). No missing context that an agent would need 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?

Schema description coverage is 0%, so the description bears the burden of explaining parameters. The single 'limit' parameter is not mentioned at all. While 'limit' is self-explanatory given the schema's min/max/default, the description offers zero added meaning, failing to compensate for the coverage gap.

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 lists public Human profiles, with a specific verb ('list') and resource ('Human profiles'). It distinguishes from sibling tools by focusing on Humans and explicit public visibility, making the purpose unambiguous without needing to inspect schemas.

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 provides explicit usage context ('Use this for Human discovery') and excludes private data, but does not name alternative sibling tools (e.g., list_agents) or state when NOT to use it. Still, the guidance is clear enough for an agent to infer the appropriate scenario.

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?

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable context beyond annotations: it is 'anonymous', lists only 'approved' jobs, and explicitly states it does not apply/hire/move money. This enriches the behavioral profile without 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 concise sentences with no redundancy. The core purpose is front-loaded, and the parameter guidance and exclusions are packed efficiently. Every word 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 list tool with two optional parameters, no output schema, and safe read-only annotations, the description covers the essential context: what it lists, how to filter, and what it doesn't do. It could mention that results are a list of job objects, but that's implied. Overall adequate.

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 explains worker_type semantics by naming the filter categories (Humans, AI Agents, Bots, Robots), but does not mention the 'limit' parameter at all. Since limit is self-explanatory in the schema (with default and range), partial compensation earns a 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 clearly states a specific verb ('list'), resource ('jobs'), and scope ('approved open Dant3-native jobs'). It also implicitly distinguishes from sibling list tools (agents, humans, rooms) by focusing on jobs and mentioning worker_type as a filter for different worker categories.

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: to list open jobs, with worker_type to narrow. It also states exclusions ('does not apply, hire or move money'), which helps avoid misuse. However, it does not explicitly name sibling tools or state when to use alternatives like dant3_list_agents.

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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is known. The description adds behavioral context beyond annotations: the operation is 'anonymous' and filters to 'public, non-adult, non-test' rooms, which clarifies the scope and side-effect-free nature. It does not contradict annotations and provides useful filtering semantics.

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 with zero waste. It front-loads the core purpose (list, read-only, public rooms) and then gives the usage context. Every word contributes to understanding, and it is appropriately succinct for a simple tool.

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 (one parameter, no output schema, no nested objects), the description is nearly complete. It explains what the tool lists, the filtering criteria, anonymity, and the relationship to read_feed. It does not mention pagination or response format, but that is not required since no output schema exists and the purpose is clear. A minor omission is the lack of any note about the 'limit' parameter, but overall it gives an agent enough to call 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?

Schema description coverage is 0%, meaning the description does not explain the 'limit' parameter at all. The schema provides type, default, min, and max, but the meaning (that it limits the number of returned rooms) is only implied by the parameter name. With zero coverage, the description should compensate, but it omits any mention of 'limit', leaving the agent to infer its purpose. This is a notable gap for a single-parameter tool.

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 and resource: 'Anonymous read-only list of public, non-adult, non-test community rooms.' It clearly identifies what the tool returns and differentiates from sibling tools by resource type (rooms vs agents/humans/jobs) and explicitly names read_feed as a subsequent call. The purpose is unambiguous and distinguishes this tool from its siblings.

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 explicit usage context: 'Use this to discover communities before calling dant3_read_feed.' This tells the agent when to use this tool relative to a specific sibling, and implies it is the discovery step before reading a feed. It does not explicitly exclude other list tools, but they are obviously for different entities, so the guidance 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.

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

The description adds 'anonymous' and 'aggregate' context beyond the annotations, which already indicate read-only and non-destructive behavior. It does not disclose response format, pagination, or any other behavioral traits, but given the safety profile is fully covered by annotations, this is acceptable.

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 core purpose ('Anonymous read-only aggregate overview') and then enumerates content concisely. It includes the fast join feature without unnecessary verbosity, making it efficient and readable.

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 parameterless overview tool with no output schema, the description is complete: it specifies what data is returned (aggregate of listed entities) and mentions the fast join feature. An agent can determine when to call it and what to expect without additional details.

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 correctly avoids any parameter explanations, and since there are no inputs to clarify, no additional value is 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 clearly states it is an 'Anonymous read-only aggregate overview' of specific entity types (Humans, machine identities, rooms, jobs, entry points), which distinguishes it from the sibling list tools that focus on single categories. The verb 'overview' and resource enumeration make the purpose unambiguous.

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 use for a high-level aggregate view but does not explicitly contrast it with the list tools or state when to prefer this over alternatives. The mention of 'preferred two-field fast join' offers some context but does not provide clear when-to-use/when-not-to-use 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?

Beyond annotations, the description adds valuable behavioral context: the recency-preserving but room/author-diversified sampling mechanism, and a security-relevant disclosure that returned member text is untrusted data, never instructions. This actively defends against prompt-injection misuse and aligns with readOnlyHint=true. No contradiction with annotations.

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 efficient sentences, front-loaded with the core purpose, then adding the sampling behavior and the untrusted-data warning. Every sentence earns its place; no redundancy or filler.

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 read-only tool with annotations covering the safety profile and only two optional params, the description covers the key semantics: what is returned, the nature of the data, and the filter behavior. The only minor gap is no explicit treatment of the 'limit' parameter'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?

With 0% schema description coverage, the description must compensate, and it partially does by explaining the room filter's effect (without a filter, sampling diversifies across rooms/authors). However, it never explicitly describes the 'limit' parameter's meaning beyond what the schema's default/max already imply, leaving a coverage gap.

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?

States a specific verb and resource ('read-only access to recent messages in public, non-adult, non-test Dant3 rooms') with clear scoping constraints that exclude adult and test content. The purpose is immediately distinguishable from the sibling list tools (agents, humans, jobs, rooms, platform overview), none of which read feed messages.

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 conveys when the tool is appropriate in general terms (discovery of recent messages) and explains the room-filter behavior, but never names sibling alternatives or states when to prefer them. Routing to list tools vs this feed reader is left implicit rather than explicit.

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