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 states the tool 'Get[s] summary statistics', implying a read-only operation, but doesn't specify details like what data is included in the statistics, whether it's real-time or cached, or any performance considerations. This leaves significant gaps in understanding the tool's behavior.

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, clear sentence that directly states the tool's function without any unnecessary words or fluff. It's front-loaded with the core purpose, making it efficient and easy to parse for an AI agent.

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 has no parameters and no output schema, the description provides a basic understanding of what the tool does ('Get summary statistics'), but it lacks details on what the statistics include, format, or how they relate to the collection. For a tool with no structured data to rely on, this is a minimal viable description that leaves room for improvement in context.

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 0 parameters, and the schema description coverage is 100%, so there are no parameters to document. The description appropriately doesn't add parameter details, as none are needed, and it doesn't mislead by mentioning non-existent parameters. A baseline of 4 is applied since no parameters exist, and the description doesn't compensate for any gaps.

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's purpose with a specific verb ('Get') and resource ('summary statistics about the indexed collection'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'search' or 'list_channels', which might also provide statistical or listing functions, so it doesn't reach the highest 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 no guidance on when to use this tool versus alternatives like 'search' or 'list_channels'. It lacks context about what 'summary statistics' entail or when this is preferred over other data retrieval methods, leaving the agent to infer usage 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?

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions retrieving 'all chunks' and 'including any linked resources' which provides useful context about what data is returned. However, it doesn't mention potential limitations like rate limits, authentication requirements, or what happens when thread_ts is invalid.

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 efficiently structured with a purpose statement, usage guidance, and parameter explanations in just three sentences. Every sentence earns its place, and the information is front-loaded with the most important details first.

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 operation with 2 parameters and no annotations or output schema, the description provides adequate but not complete context. It explains what the tool does and when to use it, but doesn't describe the return format, error conditions, or any behavioral constraints that would be helpful for an AI agent.

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 compensates well by explaining both parameters: 'thread_ts: The thread timestamp (from search result metadata)' and 'channel_id: Optional channel ID to narrow the lookup.' This adds meaningful context beyond what the bare schema provides about parameter purposes and sources.

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's purpose with specific verbs ('retrieve all chunks') and resources ('for a specific Slack thread, including any linked resources'). It distinguishes itself from sibling tools like 'search' by specifying it's for getting full thread context after using search().

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 guidance on when to use this tool: 'Use this after search() to get the full thread context.' This clearly positions it relative to the 'search' sibling tool and provides a specific workflow context.

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 full burden for behavioral disclosure. It states what the tool returns but doesn't address important behavioral aspects like whether results are paginated, sorted, filtered, or if there are rate limits. 'List all' suggests comprehensive retrieval but lacks operational details.

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, efficient sentence that communicates the core functionality without unnecessary words. It's front-loaded with the main action and includes the key output detail (document counts). Every word serves a purpose.

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 with no annotations and no output schema, the description provides basic purpose but lacks important context. It doesn't explain the return format, what 'indexed' means operationally, or how results are structured. The absence of output schema increases the need for return value description.

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 with 100% schema description coverage, so the baseline is 4. The description appropriately doesn't waste space discussing non-existent parameters, maintaining focus on the tool's purpose.

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 verb ('List') and resource ('indexed channels'), and specifies what information is returned ('their document counts'). It doesn't explicitly differentiate from sibling tools like 'list_users' or 'collection_stats', but the resource specificity provides reasonable distinction.

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 about when to use this tool versus alternatives like 'collection_stats' or 'search'. The description implies this is for getting a comprehensive list of channels with document counts, but doesn't specify use cases, prerequisites, or exclusions.

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 effectively describes the tool's behavior: scanning metadata to return unique names, implying a read-only operation without destructive effects. However, it lacks details on potential limitations like rate limits, error conditions, or data freshness, which would be helpful for a tool with no annotation coverage.

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 core purpose in the first sentence, followed by operational details and usage guidance in two additional sentences. Every sentence earns its place by adding distinct value: listing participants, explaining the scanning method, and stating the utility for another tool, with zero wasted words.

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 (0 parameters, no annotations, no output schema), the description is largely complete. It covers purpose, behavior, and usage context effectively. A minor gap is the lack of output format details (e.g., structure of returned names), but this is mitigated by the tool's straightforward nature and the explicit mention of 'unique participant names'.

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 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately adds context about the tool's output ('unique participant names') and its relationship to other parameters ('user_filter in search()'), providing value beyond the empty schema without redundancy.

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 specific action ('List all known participants') and resource ('across indexed Slack threads'), distinguishing it from siblings like 'list_channels' or 'search' by focusing on thread participants rather than channels or content. It explicitly mentions scanning 'thread_users metadata field' for 'unique participant names', providing precise operational 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 explicitly states when to use this tool: 'Useful for discovering valid values for the user_filter parameter in search().' This provides clear guidance on its purpose as a helper for another tool ('search'), distinguishing it from alternatives like 'get_thread' or 'collection_stats' without needing to list exclusions.

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 full burden and adds valuable behavioral context: it explains the search method (semantic similarity with Cohere reranking), filtering capabilities (source, channel, user), and the include_links parameter's effect on results. However, it doesn't mention rate limits, authentication needs, or error handling, leaving some gaps.

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 and front-loaded with the core purpose, followed by a clear Args section. Each sentence adds value: the first explains the search method, and subsequent lines efficiently detail parameters without redundancy. It's appropriately sized for a 6-parameter 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 6 parameters, 0% schema coverage, no annotations, and no output schema, the description does an excellent job explaining inputs and behavior. However, it lacks details on output format (e.g., result structure) and error conditions, which would help the agent use it more effectively. It's nearly complete but has minor 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 provides detailed semantics for all 6 parameters: query purpose, n_results defaults and limits, source_filter enum values, channel_filter/user_filter matching behavior, and include_links effect. 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 clearly states the tool's purpose: 'Search the Slack index using semantic similarity with Cohere reranking.' It specifies the verb ('Search'), resource ('Slack index'), and method ('semantic similarity with Cohere reranking'), distinguishing it from siblings like list_channels or get_thread that perform different 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 implies usage through parameter explanations (e.g., source_filter options, include_links behavior) but does not explicitly state when to use this tool versus alternatives like collection_stats or get_thread. It provides context for filtering but lacks direct guidance on tool selection scenarios.

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