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

Annotations indicate readOnlyHint=false, idempotentHint=false, destructiveHint=false. The description adds meaningful behavioral context: it specifies the message can be outgoing or incoming, private or public, and explicitly states the return value includes created message details. 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?

The description is well-structured with clear sections (Args, Returns, Examples). Every sentence earns its place, and the examples are concise yet illustrative. No fluff or repetition of what the schema already 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?

Despite lacking an output schema, the description explicitly states what the tool returns. It covers the main use cases (public reply, internal note) and explains defaults. Given the tool's complexity (4 params, two required), this is complete enough for an agent to use it 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 covers all 4 parameters (100% coverage), so baseline is 3. The description adds value by restating parameters in a compact Args section, clarifying default values for message_type and private, and providing two concrete examples that demonstrate parameter usage and practical intent.

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 "Create a new message in a Chatwoot conversation." It uses a specific verb ('create') and resource ('message'), and naturally distinguishes from sibling tools that list, get, or filter conversations/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 provides practical guidance with examples for both public replies and private internal notes, clarifying when each mode is appropriate. However, it does not explicitly mention alternatives or when not to use this tool (e.g., 'for reading messages use chatwoot_list_messages'), though the context makes it obvious.

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?

Even with annotations declaring the tool as read-only, idempotent, and non-destructive, the description adds substantial behavioral context. It exposes the exclusive date boundary semantics (e.g., date_from='2026-02-20' means Feb 21+), pagination behavior (25 per page, all_pages option), and the returned meta counts. These details go well beyond what annotations 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?

The description is long but logically structured: a summary sentence, an IMPORTANT caveat about date format, an Args list, a Returns section, and five practical examples. It is front-loaded with the purpose and critical date semantics, and every section earns its place—no filler or redundancy exists.

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, the description fully covers what the tool returns ('Filtered conversations with meta counts') and comprehensively documents all 11 optional parameters, including edge cases and examples. For a complex query tool with multiple filters and pagination modes, the description is entirely sufficient for an agent to select and invoke 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?

The input schema already has 100% description coverage, setting a baseline of 3. The description reinforces this with a dedicated Args list and, more importantly, clarifies the date-exclusivity rule with concrete examples (e.g., 'Get all conversations from Feb 21'). This adds meaningful value beyond the schema text, warranting a 4.

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 opens with 'Filter conversations using advanced criteria including date ranges, status, assignee, and labels,' which clearly states the verb (filter), resource (conversations), and scope. It differentiates from the simpler sibling 'chatwoot_list_conversations' by emphasizing 'advanced criteria' and the dedicated filter API, making the tool's unique 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 provides clear context for when to use this tool: whenever advanced filtering by date, status, assignee, or labels is needed. It explains the filter API and pagination options, but it does not explicitly mention alternative tools or exclusion cases, so it falls short of full sibling differentiation.

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, idempotentHint=true, and destructiveHint=false. The description adds return content details (status, inbox, contact, assignee, labels, message stats), which enriches the behavioral profile beyond the annotation flags. 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?

The description is well-structured with Args, Returns, and Examples sections. The first sentence front-loads the purpose, and each section adds specific information without excess verbosity.

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 read-only nature, the description covers purpose, parameters with defaults, return contents, and an example. No output schema exists, but the Returns section lists key fields. Annotations cover safety hints. This is complete for an agent to select and invoke 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?

Both parameters are fully described in the schema (conversation_id, response_format). The description restates them and adds a usage example with conversation_id: 123, giving practical invocation context. Schema coverage is 100%, so the baseline is 3; the example lifts it to 4.

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 opens with 'Get detailed information about a specific Chatwoot conversation' and 'retrieves full details for a single conversation,' clearly distinguishing it from sibling tools that list or filter conversations. The verb 'Get' and resource 'specific conversation' are specific and 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 states it is for a 'single conversation' and lists the contents (contact info, assignee, labels, custom attributes), implying use when you have a conversation ID. It does not explicitly mention alternatives like list_conversations, but the 'specific' scope provides context. No exclusions are stated.

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 read-only, idempotent, and non-destructive behavior. The description adds valuable context beyond annotations: it explicitly mentions pagination support, the output format options (markdown/json), and details of the return content. This provides a richer picture of the tool's behavior without contradicting 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?

The description is structured with clear sections (Args, Returns, Examples) and front-loaded with the primary Purpose. While it is longer than the minimal two-sentence example, every section adds necessary information for a tool with five parameters and no output schema. The use of bullet-style lists keeps it scannable and avoids unnecessary prose.

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 listing tool with no output schema, the description is complete: it explains the return fields, provides examples for common use cases, and documents defaults. It does not, however, address potential edge cases like empty results, error conditions, or rate limits, which would require additional context. Given the moderate complexity and full parameter schema, this is a solid but not 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?

Schema coverage is 100%, so the baseline is 3. The description enriches parameter semantics by explaining defaults (page: 1, status: 'open', response_format: 'markdown'), enumerating allowed values for status and assignee_type, and providing concrete examples of parameter combinations. These additions go beyond the schema's descriptive text.

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 'conversations', clearly stating the tool retrieves conversations with filtering options. It does not explicitly distinguish this from the sibling 'chatwoot_filter_conversations', which appears to have overlapping purpose, preventing a perfect score.

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 examples of parameter usage but no guidance on when to use this tool versus the sibling 'chatwoot_filter_conversations' or 'chatwoot_list_messages'. There is no mention of scenarios where this tool is preferred or excluded, leaving the selection decision to the agent without explicit direction.

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, idempotentHint=true, and destructiveHint=false, covering safety. The description adds valuable behavioral detail beyond annotations by specifying exactly what is retrieved (message content, sender information, timestamps, attachments) and the return structure (message type, private flag). This provides transparency about the tool's output and side-effect-free nature without contradicting 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?

The description is well-structured with a clear purpose line, a concise Args section, a Returns section, and an example. Every sentence serves a purpose—no filler or repetition. It is front-loaded with the action and scope, making it easy for an agent to quickly understand what the tool does. The length is appropriate for the amount of detail provided.

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 parameters and no output schema, the description is highly complete. It explains both parameters, the response fields (message ID, content, type, sender, timestamp, attachments, private flag), and provides a concrete example. While it doesn't mention pagination or error handling, these are not critical for this tool's basic use case, and the description fully equips an agent to invoke it 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 100%, so the schema already documents both parameters thoroughly. The description repeats this information in an 'Args' section and adds a usage example, which is helpful but largely redundant. Since the schema carries the heavy lifting, the description adds marginal value beyond schema descriptions, keeping this at the baseline of 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 opens with a specific verb and resource: 'List all messages in a Chatwoot conversation.' It clearly identifies the scope (a single conversation) and the content (messages with sender info, timestamps, attachments). This distinguishes it from sibling tools like chatwoot_list_conversations (lists conversations) and chatwoot_get_conversation (retrieves a conversation's details).

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 clearly states the primary use case: retrieving all messages from a specific conversation. It implies when to use this tool (when you need message-level detail) and implicitly differentiates it from siblings by focusing on messages rather than conversations or creating messages. However, it does not explicitly mention when not to use it or name alternative tools, so it misses the full criteria for a 5.

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