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

No annotations provided, so description carries full burden. It discloses the result content (domains, trust scores, capabilities) but omits behavioral traits like authentication requirements, rate limits, data freshness, or whether results are paginated.

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?

Single sentence front-loaded with action and scope. Every word serves the purpose; no fluff.

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 parameters, no output schema, and simple purpose, the description is mostly complete for a list tool. However, lacks details on result format or pagination behavior, which could be relevant for agents.

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 has zero parameters with 100% coverage, so description needs no parameter details. Baseline 4 is appropriate; description adds no param info but also requires none.

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 uses specific verbs and resources: 'List all registered agents' with scope '.aint domains' and listed attributes 'trust scores, and capabilities.' It effectively distinguishes from siblings like ipoll_pull (deletion/retrieval?) and ipoll_send (sending).

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 when-to-use or when-not-to-use guidance. The context is implied by sibling tool names (e.g., ipoll_pull likely for pulling specific data, ipoll_send for sending), but no explicit comparison 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. It discloses the side effect of mark_read ('Mark messages as read after pulling') and specifies return fields (sender, content, type, timestamp), adding behavioral context beyond a simple read.

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 efficient with one summary line followed by clearly labeled Args and Returns sections, front-loading the purpose and avoiding any redundant or vague 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?

The description covers purpose, parameters, and return format. It does not explicitly mention that agent_id has a default value (present in schema), but overall it is sufficiently complete for an agent to use 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%, and the description compensates fully by explaining each parameter: 'agent_id: Your agent ID to check inbox for' and 'mark_read: Mark messages as read after pulling,' adding meaning beyond bare schema titles and defaults.

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 'Pull messages from your I-Poll inbox,' clearly specifying the verb (pull), resource (messages), and scope (inbox). This distinguishes it from siblings like ipoll_send (send) and ipoll_status (status).

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 checking inbox messages but does not explicitly state when to use this tool versus alternatives, nor does it provide 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?

With no annotations, the description carries the full burden. It discloses that the tool performs a lookup and returns capabilities, trust score, endpoint, and registration date. However, it does not mention side effects, idempotency, authentication, or rate limits.

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 concise: a short analogy, then structured Args and Returns sections. Every sentence serves a purpose with no redundancy or fluff.

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 (one parameter, no output schema), the description adequately covers what the tool does, its inputs, and its return fields. Minor omissions like error handling or pagination do not significantly detract.

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 provides no parameter descriptions (0% coverage). The description adds meaning by explaining the 'domain' parameter as an agent name or .aint domain with examples ('root_idd' or 'root_idd.aint'), which compensates well for the schema 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 the tool resolves .aint domains, analogous to DNS for AI agents. The verb 'resolve' and resource '.aint domain' are specific, but it does not explicitly differentiate from sibling tools like ipoll_agents or ipoll_status.

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 looking up agent information by domain ('like DNS'), but it does not provide explicit when-to-use, when-not-to-use, or alternative tools. The sibling tools are listed without 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 full burden. It mentions the return type ('Delivery confirmation with message ID') and explains poll types, but it does not explicitly state side effects, failure modes, or whether the operation is safe/read-only.

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 main purpose, followed by a structured Args and Returns section. It is concise without extra fluff, though the bulleted list could be slightly more compact.

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 description covers all parameters, explains the protocol context, and includes return behavior. Given the absence of an output schema, the description provides adequate completeness for a messaging tool with 5 parameters.

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 compensates fully by explaining each parameter: to_agent (with examples), content, from_agent (default), poll_type (enumeration of values), and metadata (optional extra data). This adds significant meaning beyond the bare schema.

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 starts with 'Send a message to an AI agent via I-Poll,' clearly stating the verb and resource. It distinguishes itself from sibling tools like ipoll_agents, ipoll_pull, etc., which handle other operations.

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 I-Poll protocol and provides examples for to_agent and meaning of poll_type. It is clear when to use this tool (to send messages), though it does not explicitly list exclusions or alternatives beyond the sibling names.

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 bears full burden. It states the tool shows 'status' but does not explicitly indicate it is read-only or disclose any authentication needs, rate limits, or other behavioral traits.

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 very concise (single line) and front-loaded. It conveys the core purpose without unnecessary words, though it could benefit from slightly more structure.

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 simplicity of the tool (0 params, no output schema), the description is minimal. It does not explain the format of the returned status or what 'health' entails, leaving the agent uncertain about the output.

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 coverage is 100%. The description does not need to add parameter details, and it provides sufficient context for the parameterless interface.

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 shows 'I-Poll system status — agents, queues, and health,' using a specific verb and resource. It distinguishes from sibling tools like ipoll_agents (agent-specific) and action tools (ipoll_pull, etc.).

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 does not explicitly state when to use this tool versus alternatives. Usage is implied by the name and context, but no direct guidance or exclusions are provided.

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