SingChat

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

No annotations exist, so the description carries the full burden. It discloses asynchronous ingestion, PENDING initial status, and the need to poll list_knowledge for readiness. Also mentions the 50-page crawl limit, which is valuable behavioral context.

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 sentences, no wasted words. Key information is front-loaded: purpose first, then type differentiation, then async behavior. Every sentence adds unique 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?

Given the tool's complexity (two modes, async, polling), the description covers the essential workflow. It mentions the status polling with list_knowledge, which compensates for the lack of an output schema. Could be improved by noting error scenarios or prerequisites, but still 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?

Parameter schema coverage is 100%, so the schema already documents parameters well. The description adds value by explaining the 50-page limit for url and the polling behavior, which goes beyond the schema's static definitions.

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 a clear verb-resource pair: 'Train the AI agent by adding a knowledge source.' It explicitly distinguishes between two modes ('url' vs 'qa') and references a sibling tool ('list_knowledge') for status polling, making differentiation easy.

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 when to use each type (url for crawling, qa for Q&A) and mentions asynchronous ingestion with status polling via list_knowledge. It does not explicitly state when not to use, but provides enough context for correct selection.

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 provided, so description carries full burden. It discloses the action (returns a link), states it permanently secures the workspace, and mentions API key requirement. Lacks details on failure modes or idempotency, but adequate given simplicity.

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, front-loaded with key information, no redundant words. Efficiently communicates purpose, usage, and requirement.

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, no-output-schema tool, the description is complete: states what it does, when to use, and what is required. No significant 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?

No parameters in schema, so baseline 4 applies. Description adds no parameter information because none exist; schema coverage is 100%.

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?

Description clearly states the tool returns a link to claim a SingChat workspace by setting email and password, specifies context of auto-created workspaces via MCP, and distinguishes from sibling tools like singchat_setup by targeting a specific use case.

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?

Explicitly states when to use (for auto-created workspaces via MCP) and a prerequisite (requires workspace API key). Could improve by noting when not to use (e.g., manually created workspaces), but still clear 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?

With no annotations provided, the description carries full burden. It discloses the partial update behavior, the read-only call, and that API keys are unaffected. This is good transparency, though it could mention permission requirements or idempotency.

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 three sentences with no wasted words. Every sentence provides essential information: what the tool does, update semantics, and a safety note about API keys. It is front-loaded and concise.

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 five parameters all documented in schema, no output schema, and no nested objects, the description provides sufficient context about the tool's behavior. It covers the key aspects, though it does not mention authentication or error 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 100%, so baseline is 3. The description adds meaning by stating the partial update behavior and the read-back use case, which are not in the schema. This adds value beyond the 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 configures the workspace's AI support agent. It uses a specific verb ('Configure') and resource ('AI support agent'), making the purpose clear. However, it does not explicitly distinguish this tool from siblings like 'add_knowledge' or 'search', which have different purposes.

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 excellent usage guidance: all fields are optional, partial update semantics are explained, and calling with no fields reads current config. It also clarifies that API keys are not touched. However, it does not explicitly state when to avoid using this tool or suggest alternatives.

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?

Discloses returned fields and conditional behavior (workspace vs help article) despite no annotations. Full disclosure of important 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?

Three concise sentences, no redundancy. Front-loaded with essential 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 single-parameter tool with output schema, description covers behavior, return fields, and conditional logic. Complete and self-sufficient.

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 100% coverage for the one parameter, and description adds clarifying context that the id is from search tool, enhancing understanding beyond 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?

Clearly states it fetches full text by id, distinguishing from sibling tools like search and list_knowledge. Verb+resource is specific.

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?

Explicitly says id comes from search tool and explains behavior based on API key presence, guiding when to use. Lacks explicit when-not-to-use but context is clear.

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 of behavioral disclosure. It clearly indicates a read operation ('Return'), lists what is returned, and mentions default parameters. It does not contradict any annotations and provides sufficient transparency for a compact analytics 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?

The description is a single sentence that is front-loaded with the main purpose, contains no redundant information, and every part earns its place by specifying what the tool returns.

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 is complete. It explains both the parameter's role and the return content without needing additional detail.

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% and the schema already describes the 'days' parameter as a look-back window. The description adds minimal new semantic value (e.g., default value), but since coverage is high, a baseline score of 3 is appropriate.

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 'Return a compact analytics summary' and specifies the exact metrics included (conversation counts, resolution rate, message volume, AI share, live snapshot). This distinctively separates it from sibling tools like get_conversation or list_conversations which deal with individual conversations or lists.

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 obtaining analytics, but it does not provide explicit guidance on when to use this tool versus alternatives (e.g., search or list_conversations). No exclusions or alternatives are mentioned, leaving the agent to infer context from the description alone.

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?

Although no annotations, description discloses that it returns contact details and recent messages ordered oldest→newest. However, it doesn't state max messages, read-only nature, or error handling.

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, no wasted words. Perfectly concise.

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?

Adequate for a simple fetch tool with one parameter and no output schema; covers main output 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?

Schema coverage is 100%, but description adds context that the conversationId comes from list_conversations, which helps the agent understand the source.

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 'Fetch' and resource 'conversation', and clearly distinguishes from siblings like list_conversations (list vs single) and reply_to_conversation (action vs fetch).

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?

Implies usage when you have a conversationId from list_conversations, but no explicit when-not or alternative tools mentioned.

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 provided, so description carries full burden. Discloses it returns embed code and needs API key, but does not mention error handling, rate limits, or if operation is read-only (likely safe). Adequate but not exhaustive.

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 concise sentences. Front-loaded with action and result. Each sentence adds unique information: what it returns, prerequisites, optional filtering.

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?

No output schema. Description says 'return the embed code' but does not specify response format (e.g., object with keys, string). Could be more complete about what the agent receives.

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%. Description adds value by explaining that omitting 'platform' returns all three flavors and lists the enum values (html, react, next). Goes beyond schema details.

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?

Clearly states the tool returns SingChat chat-widget embed code for the workspace's web inbox. Specifies three flavors (plain HTML, React, Next.js). Distinct from sibling tools which deal with knowledge, analytics, conversations, 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?

Provides prerequisite (requires workspace API key). Shows optional parameter to get one flavor. Does not explicitly state when not to use, but tool is specific enough.

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 discloses the compact list response fields, ordering, and filtering behavior. It does not mention auth or rate limits, but for a list tool, this is adequate.

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 no fluff, front-loaded with purpose and key details. Every sentence contributes meaningful 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?

Covers filtering and return fields effectively for a list tool without output schema. Minor gap: does not mention pagination or limit behavior beyond what schema provides, but 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 coverage is 100% with descriptions for all three parameters. The description adds value by explaining search matches on contact name/email and last message, and clarifies the return format, going beyond the 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 it lists support conversations (agent/inbox view) ordered by newest activity first, distinguishing it from sibling tools like get_conversation (single) and search (broader). The verb 'list' and resource 'conversations' are specific.

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: workspace support conversations, optional filters, and return format. However, lacks explicit guidance on when not to use or alternatives, though context from sibling tools implies its scope.

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, description reveals it returns status and chunk counts but doesn't mention pagination or limits. Adequate for a simple list-all 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 concise sentences: first explains purpose and output, second provides usage guidance. No 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 zero parameters and no output schema, description fully covers what the tool does and when to use it. No missing critical information.

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?

No parameters exist, so parameter semantics are irrelevant. Baseline score of 4 applies as description adds no unnecessary parameter info.

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?

Description clearly states it lists all knowledge documents with processing status and chunk counts, distinguishing it from add_knowledge and search_knowledge.

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?

Explicitly says to use after adding knowledge via add_knowledge to check training status, providing clear when-to-use 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?

No annotations are provided, so the description carries full burden. It discloses that the tool performs CRUD operations (list, create, update, delete) but lacks details on permissions, side effects (e.g., impact on chat widget), or rate limits. This is adequate but not comprehensive.

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: the first states purpose and context, the second details operations. Every sentence is necessary and no words are wasted. It is front-loaded with the core 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?

The description covers the essential behavior for a CRUD tool with 5 parameters and 4 actions. However, it does not mention return values or response format (no output schema provided), leaving a minor gap in completeness.

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 baseline is 3. The description adds value by explaining the relationship between the action enum and required parameters, and by clarifying the purpose of FAQ items in the chat widget. This goes beyond the individual parameter descriptions.

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 manages FAQ items (list, create, update, delete) for the workspace, and notes they appear in the chat widget and are usable by the AI agent. This verb+resource combination is specific and distinguishes from sibling tools like add_knowledge or list_knowledge.

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 how to use the action parameter and which additional parameters are needed for each operation (e.g., create needs question+answer, update/delete need id). It provides clear context but does not explicitly state when not to use this tool or mention alternatives.

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 burden of disclosing behavioral traits. It correctly identifies the tool as a health check, implying a safe, read-only operation with no side effects. However, it does not explicitly state idempotency or rate limits, though for a simple ping these are typically assumed.

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 core purpose ('Health check') and concisely states the return values. No redundant or extraneous information is present.

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 (no parameters, no output schema), the description provides sufficient context for an agent to understand its function and output. However, it does not specify the exact format of the return value (e.g., JSON structure), which could be considered a minor gap. Overall, it is complete enough for a ping tool.

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 zero parameters, and schema description coverage is 100%. According to the rubric, 0 parameters yields a baseline of 4. The description does not need to add parameter meaning since there are none, and it correctly implies no input is required.

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 is a health check for the SingChat MCP server and specifies the return values ('pong' and authentication status). It distinguishes itself from sibling tools, which are focused on knowledge, conversations, accounts, 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?

While there are no explicit usage guidelines or alternatives to compare, the tool's purpose as a health check is self-evident. The description provides context about the return value including authentication status, which helps an agent decide when to use it (e.g., to verify server connectivity). No other sibling tool serves the same function.

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 full burden. It discloses key behaviors: it turns off automated AI handling, broadcasts the message live, and attributes the reply to a workspace owner/admin. This is good, though it omits potential side effects or error conditions.

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 three sentences, each adding value: first sentence states purpose, second and third add behavioral details. There is no filler or redundancy, making it efficient and concise.

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 tool with two simple parameters and no output schema, the description covers the main effect and side effect (taking over AI). However, it does not mention the response format or potential errors, which would round out completeness.

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 schema description coverage at 100%, the description adds no additional meaning beyond what the schema already provides for the two parameters ('content' and 'conversationId'). Baseline of 3 is appropriate.

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: 'Send an agent reply to a conversation.' It distinguishes from sibling tools by specifying that it takes over from AI and broadcasts live, which sets it apart from tools like resolve_conversation or list_conversations.

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 context for when to use the tool (to send an agent reply and take over from AI), but it does not explicitly mention when not to use or offer alternatives among sibling tools. The guidance is clear but lacks 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. It discloses that the conversation is closed and the visitor's widget stops showing it as active. This adds meaningful behavioral context beyond the schema. However, it omits details like whether the action is reversible or if there are authorization constraints.

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 extremely concise: two sentences that immediately convey the tool's purpose and effect. There is no superfluous 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 simple mutation tool with one parameter and no output schema, the description adequately explains the outcome (closes and hides from visitor). It could mention the agent's perspective or confirmation, but the key behavioral context is present.

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% for the single parameter 'conversationId'. The description does not add any semantics beyond what the schema already provides ('The conversation id to resolve.'), so the baseline of 3 applies.

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 action: 'Mark a conversation as RESOLVED (closes it).' It uses specific verbs ('resolved', 'closes') and indicates the effect on the visitor's widget, distinguishing it from sibling tools like reply_to_conversation or get_conversation.

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 is provided. The description implies usage when a conversation should be closed, but it does not mention alternatives or prerequisites. The context is clear but lacks exclusionary 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 provided, so description carries full burden. It discloses that search behavior depends on API key existence, and that results include id, title, url, with fetch needed for full text. This is good transparency 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?

Description is concise (three sentences), front-loaded with main purpose, and contains no extraneous information. Every sentence 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 search tool with one parameter and an indicated output schema, the description covers key behaviors, return format, and follow-up action. No gaps given the tool's complexity.

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% with one parameter 'query' described as 'The search query or user question.' The description does not add significant meaning beyond the schema, so baseline score of 3 is appropriate.

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?

Description clearly states the tool searches SingChat for relevant documents, distinguishes behavior with/without workspace API key, and lists return fields (id, title, url). This provides specific verb+resource and differentiates 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?

Explains when to use (searching documents) and the two contexts (workspace key vs no key). Mentions using fetch for full text, which is a follow-up step. Lacks explicit when-not-to-use or comparison to sibling tools, but context is clear.

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 full burden. It discloses return format (chunks with source title and similarity score) and a filter (READY documents). However, it does not mention pagination, rate limits, or potential errors. For a read-only search tool, this is adequate but not comprehensive.

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. Front-loaded with purpose. No redundant information. Every sentence 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?

No output schema, but description explains what is returned (chunks, source title, similarity score) and a key filter (READY only). For a simple search tool with 2 params, this is nearly complete. Minor gap: no mention of scope (workspace-level) or handling of empty results.

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 parameters are already described. Description adds no extra semantic meaning beyond stating the return format. Baseline 3 is appropriate.

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?

Explicitly states 'Semantic search over the workspace knowledge base', clearly identifying verb (search) and resource. Distinguishes from siblings like 'search' and 'list_knowledge' by specifying semantic search over knowledge base.

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 a clear condition: 'Only searches documents that have finished training (status READY).' This gives context on when results are available, though it does not explicitly compare with alternatives like 'search'.

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. The description explains the basic behavior (adds a rule, matches keywords, replies). However, it does not mention whether existing rules are overwritten or if duplicate keywords are allowed, which are relevant 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?

Two sentences, no filler. The key idea is front-loaded and the example immediately clarifies usage. Every word 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?

Given no output schema and no annotations, the description adequately covers the tool's purpose, required parameters, and usage context. It could mention success/error behavior or rule overriding, but it's sufficient for a simple creation tool.

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 baseline is 3. The description adds value by explaining the use case and giving an example, but it does not elaborate on the 'matchType' parameter beyond what the schema already provides.

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?

Description clearly states the tool adds a keyword auto-reply rule, specifies how it works (matches keywords, bot replies), and provides an example. It distinguishes itself from siblings like manage_faq or add_knowledge by focusing on automated replies.

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 says 'Good for canned FAQ-style triggers' with an example, giving context for when to use. It does not explicitly mention when not to use or alternatives, but the context is clear enough for typical use.

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. It discloses that it may create a workspace or reuse existing, never duplicates, and specifies exact return values (login credentials, API key, embed snippet). 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?

Description is a single well-structured paragraph that efficiently covers both cases (new vs existing workspace). Every sentence adds unique value with no redundancy. Front-loaded with usage instruction.

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 output schema, the description adequately explains return values and conditional behavior. For a setup tool, it covers key aspects. Lacks error handling or failure scenarios, but overall sufficiently complete.

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 one optional parameter 'name' with schema coverage 100%. The description adds context that the parameter is ignored if a key is set, which is already in schema. No additional value beyond 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 it sets up SingChat live-chat, distinguishing between creating a new workspace and returning existing setup. It uses specific verbs ('Get the user up and running', 'call this FIRST') and contrasts with sibling tools like get_embed_snippet.

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?

Explicitly says 'Call this FIRST', establishing it as the initial step. Explains conditional behavior based on API key presence. Could be improved by mentioning when not to use it (e.g., if only embed snippet needed, use sibling tool instead).

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