SingChat

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

With no annotations, the description carries the full burden. It discloses asynchronous ingestion, PENDING status, and the need to poll for READY. It also mentions the 50-page limit for url crawling. Missing details include error handling, idempotency, and potential failure modes, but core behaviors are transparent.

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 redundancy. The first sentence states the primary purpose, the second breaks down the two modes, and the third covers behavior. Information is front-loaded and every sentence is essential.

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 the invocation result (PENDING status) and next steps (poll list_knowledge). It lacks details on error states or timeouts, which would improve completeness for a moderately complex tool with two modes and async behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds significant value beyond the schema: it explains that type='url' initiates a crawl with a page limit, and type='qa' stores a single pair. It clarifies conditional requirements (url required for url, question/answer for qa). This goes beyond the schema's basic property 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 the tool's purpose: 'Train the AI agent by adding a knowledge source.' It specifies two distinct types with concrete actions: url crawling (up to 50 pages) and qa pair storage. This distinguishes it from sibling tools like 'search_knowledge' or 'list_knowledge', which serve different functions.

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 ('type='url' crawls a website; type='qa' stores a question/answer pair') and mentions polling with 'list_knowledge' for status. However, it does not explicitly contrast with sibling tools or state when not to use this tool, leaving some ambiguity for an agent.

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 fully discloses key behaviors: the link is single-use, expires in 15 minutes, and requires an authenticated legacy workspace. No contradictions or missing critical 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?

Four front-loaded sentences with no wasted words. Each sentence serves a purpose: main action, usage scenario, technical constraints, and prerequisite.

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 tool with no output schema, the description covers what it does, why to use it, key constraints, and requirements. It is fully adequate for an agent to understand invocation and consequences.

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?

There are zero parameters and schema coverage is 100%. The description adds meaning by explaining the tool's purpose and behavior beyond the schema, such as link expiration and usage context.

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 returns a link to claim a workspace by setting email/password. It distinguishes itself from siblings by mentioning auto-created workspaces via MCP, though not comparing against alternatives like singchat_setup.

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 'Use this for workspaces that were auto-created via MCP' but does not provide when-not-to-use scenarios or alternative tools. This is sufficient contextual 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. Discloses partial update behavior, read-back capability, and that API keys are not touched/returned. Provides sufficient behavioral insight for a non-destructive configuration 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?

Three short sentences, front-loaded with core purpose. Every sentence adds essential information without redundancy. Highly efficient.

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 compensates by explaining the read-back behavior. Covers optional parameters, partial update semantics, and safety (no API keys). Missing details on validation or error handling, but adequate for a simple config 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 coverage is 100% with parameter descriptions, so baseline is 3. The description adds value beyond schema by stating all fields are optional and explaining the read-back pattern. Adds context on API key handling.

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 'Configure this workspace's AI support agent', using a specific verb and resource. Differentiates from sibling tools which deal with knowledge, conversations, analytics, etc. No ambiguity.

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: update agent settings, with optional fields and the ability to read current config by calling with no fields. Mentions what is NOT done (no API keys). Lacks explicit when-not-to-use or alternatives, 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?

Discloses return fields and auth-dependent behavior (workspace vs help article), but no annotations provided; could mention error cases 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?

Two concise sentences with no redundancy, front-loading the core action and then adding essential context.

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?

Adequately covers purpose, input, auth, and output for a simple fetch operation; minor gaps like potential error conditions or scope limitations.

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 the single parameter fully; description adds meaningful context about its origin (from search) and behavior based on API key.

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 fetches full text of a single document by id from the search tool, distinguishing it from search and other tools.

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 ties usage to a prior search call and explains varying behavior based on API key presence, though does not explicitly exclude 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 full burden of disclosing behavioral traits. It explains what the tool returns (compact summary, specific metrics, live snapshot in UTC) but does not mention whether it is read-only, safe, or if there are any side effects. It is not misleading but lacks complete transparency about the operation's nature.

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 efficiently conveys the tool's output components and the parameter default. It is well-structured, front-loaded with the main purpose, and contains no superfluous 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?

The tool has one parameter, no output schema, and moderate complexity. The description explains the return value in reasonable detail (conversation counts, resolution rate, message volume, AI share, live snapshot in UTC). It lacks mention of error handling or response format, but for a simple analytics tool these are less critical. Overall, it provides sufficient context for correct usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already fully describes the single parameter 'days' (type, default, min/max, description). The description adds minimal extra meaning by reiterating the default of 30 days and placing it in the context of the analytics summary, but it does not provide new semantic information. With 100% schema coverage, a 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 returns a 'compact analytics summary' for the workspace, specifying the metrics (conversation counts, resolution rate, message volume, AI share, live snapshot). The verb 'Return' and resource 'analytics summary' are specific, and the tool is easily distinguished from siblings like get_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 no guidance on when to use this tool versus alternative tools (e.g., get_conversation, list_conversations). It does not mention when not to use it or suggest any prerequisites. The usage context is only implied by the tool's purpose.

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?

Without annotations, the description carries the burden; it specifies that messages are returned oldest-to-newest, but lacks details on auth, rate limits, or destructive potential.

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 the verb and resource, no unnecessary 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?

For a simple 1-param tool with no output schema, the description adequately explains what is returned, though it could mention pagination or size limits.

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 context about what the response includes but not param-specific details 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 the tool fetches a single conversation with contact details and recent messages, distinguishing it from siblings like 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?

No explicit guidance on when to use this tool versus alternatives; usage is implied but not clarified.

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 returns embed code in three flavors, optionally filtered by platform, and requires an API key. It implies a read-only operation but does not explicitly state 'read-only' or discuss side effects 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?

Two sentences, front-loaded with the core action. Every clause adds value without redundancy.

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 tool with one optional parameter and no output schema, the description is substantially complete. It covers purpose, parameter behavior, and a requirement. It does not describe error conditions or response format, but these are not critical given the tool's simplicity.

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%. The description adds meaning beyond the schema by explaining that omitting 'platform' returns all three flavors, and that passing it returns just one. This clarifies the behavior of the optional parameter.

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 ('return'), the resource ('SingChat chat-widget embed code'), and the scope ('this workspace's web inbox'). It specifies three flavors and distinguishes itself from sibling tools which are unrelated.

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 the tool (to get embed code) and mentions a prerequisite ('Requires a workspace API key'). However, it does not provide explicit guidance on when not to use it or alternatives, though none are needed.

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 sort order (newest first), return fields (compact list with specific fields), and optional filters. Missing details on pagination behavior, rate limits, or auth requirements, but for a read-only list tool, the transparency 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, each dense with information. First sentence gives purpose and sort order; second covers optional filters and return fields. No redundant or superfluous words. Excellent front-loading of key info.

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, description explains return fields. Covers purpose, filters, sort, and output. Missing mention of pagination mechanics (e.g., using limit for page size, but no cursor). Slight gap but otherwise complete for a simple list 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 coverage is 100%, but description adds value by clarifying the 'search' parameter (free-text on contact name/email and last message) and stating the default for 'limit' (100). The enum for 'status' is replicated but schema already lists values. Overall, description enhances parameter understanding 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 verb 'List', the resource 'conversations', and the context (workspace's support conversations, agent/inbox view). It distinguishes from siblings like get_conversation (single) and resolve_conversation (action) by specifying the listing nature and sort order.

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 when to use (when needing a list of conversations) and specifies optional filters (status, search). However, it does not explicitly mention when not to use or compare to the sibling 'search' tool, which may be for more extensive search. Lacks explicit exclusions or 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 full burden. It discloses it lists all documents with status and chunk counts, implying read-only behavior, but does not mention authorization needs, rate limits, or if there is any ordering/pagination.

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 short sentences, no fluff. The first sentence front-loads the main action, and the second adds a useful use case.

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 tool is simple (list with no params), but the description lacks details about the response format beyond status and chunk counts. It doesn't mention other fields like id or title, which would be helpful. Without an output schema, the description should provide more 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?

There are zero parameters, so baseline is 4. The description does not need to add parameter details as the schema is empty and fully covered.

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 knowledge documents with processing status and chunk counts, and explicitly distinguishes from the sibling tool add_knowledge by mentioning 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?

The description directly states when to use this tool: to check if items added via add_knowledge have finished training. However, it does not explicitly mention when not to use it or alternative tools like search_knowledge.

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 that operations affect the workspace's FAQ items and that they appear in the chat widget and are AI-usable. No annotations present, so description carries full burden; it lacks details on side effects (e.g., update is partial, deletion permanent) and 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?

Two concise sentences: first defines scope, second maps actions to requirements. No fluff, front-loaded with 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?

Covers the four operations and their required parameters. Lacks return value descriptions (no output schema) and does not explain the enabled field or default behavior. Adequate for a CRUD tool but not comprehensive.

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% description coverage, so baseline is 3. The description adds value by explicitly mapping which parameters are required per action (e.g., 'create' needs question+answer), which is not fully captured in the schema's required array.

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 lists, creates, updates, or deletes FAQ items for the workspace. Distinguishes from sibling knowledge tools by specifying 'FAQ items (shown in the chat widget and usable by the AI agent)'.

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 describes the four action values and their required parameters. No explicit when-not-to-use or alternatives, but the action enum and required fields provide clear usage 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, the description carries the full burden. It transparently describes returning 'pong' and authentication status, which covers the key behavioral aspect. No hidden side effects are relevant. However, it could mention the expected HTTP-like response 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?

Two sentences, front-loaded with the purpose, and no wasted words. Every sentence adds value: what it does and what it 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?

For a zero-parameter tool with no output schema, the description is complete. It tells the agent exactly what to expect, which is sufficient for invoking 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?

There are no parameters (0 params, 100% schema coverage). The description adds no parameter info because none exist. The baseline for 0 params is 4, which 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 it is a health check for the SingChat MCP server, with a specific verb–resource pair. It returns 'pong' and authentication status, which distinguishes it from all sibling tools that perform actions or queries.

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 it's obvious that a ping is used for health checking, the description does not explicitly guide when to use it (e.g., before other calls) or mention alternatives. The context is clear, but no when-not-to-use guidance is provided.

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?

Describes key behavioral traits: takes over conversation from AI, broadcasts message to both visitor and agent, and rejects unsupported channels before any change. No annotations provided, so description carries full burden and does so thoroughly.

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 with no redundancy. Every sentence adds value: purpose, effect, and constraint. Front-loaded with clear verb and resource.

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?

Adequately covers core functionality, effect, and constraints for a simple tool with 2 params and no output schema. Lacks post-condition description (e.g., confirmation of success or failure), but overall sufficient given low 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 covers both parameters with descriptions. The tool description adds no new meaning beyond the schema; the channel rejection context is behavioral, not param-specific. Baseline 3 justified by 100% schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states the tool sends an agent reply through a SingChat real-time inbox, taking over from AI and broadcasting to visitor and agent. Distinguishes from read-only or resolution tools like get_conversation and resolve_conversation. Also specifies which channels are rejected.

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?

Implicitly indicates when to use: when a human reply is needed that takes over from AI. Excludes email, Telegram, and WhatsApp explicitly. However, no explicit comparison to sibling tools or mention of prerequisites.

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 should disclose more behavioral traits. It mentions closing and widget visibility but omits permissions needed, reversibility, and 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?

Two sentences with no wasted words. The first states the action, the second explains the consequence. Perfectly concise and front-loaded.

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 tool with one parameter and no output schema, the description adequately covers purpose and effect. However, missing info on errors or idempotency prevents a higher 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 description coverage is 100% (the parameter's description in schema is sufficient). The tool description adds no extra information beyond that, so it meets the baseline.

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 action ('mark as RESOLVED') and the resource ('conversation'), differentiating it from siblings like get_conversation (read) and reply_to_conversation (write). It also specifies the effect on the visitor's widget.

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 when to use (to close a conversation) but does not provide explicit guidance on when not to use, nor does it mention alternatives such as whether reply_to_conversation also affects resolution.

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 exist, so description carries full burden. Discloses dual behavior based on API key, return format (id, title, url). No mention of side effects or limitations, but sufficient 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?

Four concise sentences, each adding value: purpose, context-dependent behavior, return fields, and post-fetch guidance. No redundancy.

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 search tool with output schema and related tools, the description covers behavior, return format, and next steps (fetch). No 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 provides 100% coverage for the single parameter 'query' with description. Description does not add extra semantics beyond schema, so baseline 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?

Clearly states it searches for documents, distinguishes between workspace-specific and general help content based on API key. Differentiates from sibling 'search_knowledge' by explaining scope.

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 context on when to use the tool (with or without workspace key), and recommends using 'fetch' for full text. Lacks explicit alternatives or when-not-to-use guidance.

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

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

No annotations provided, so description carries full burden. It discloses that the tool only queries trained documents and returns specific fields. Read-only nature is implied and not contradictory.

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 wasted words. First sentence states purpose, second adds a critical constraint. Information is front-loaded and efficient.

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 adequately explains return format. Constraints are clearly stated. For a search tool, it covers all essential aspects and works well with sibling 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?

Schema coverage is 100%, but description adds value by explaining the return format (chunks with title and similarity) which relates to parameter effects. This goes beyond what the schema 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 states 'semantic search over the workspace knowledge base' with clear verb 'search' and resource 'knowledge base'. It specifies return format (chunks with source title and similarity score). Distinguishes from siblings like list_knowledge and add_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?

Provides a key usage condition: only searches documents with status READY. Does not explicitly mention when not to use or compare to other search tools, but the 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 must convey behavioral traits. It describes the core behavior but omits details like case-insensitivity of keywords, default matchType, and whether rules are additive or overwrite existing ones. This adequately informs basic use but lacks depth.

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 long, efficient, and front-loaded. It immediately states the action and provides a clear example with no superfluous 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 (3 parameters, no output schema), the description covers the essential functionality and a typical use case. It could mention the default matchType or case-insensitivity, but the schema handles those details, so it is 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?

Schema description coverage is 100%, so the description does not need to elaborate on parameters. The tool description adds no extra meaning beyond what the schema already provides, meeting the baseline for high coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: adding a keyword auto-reply rule. It explains the trigger condition (inbound message matches keywords) and the action (bot replies with given text), with a concrete example. This distinguishes it from siblings like manage_faq or add_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 notes it is 'good for canned FAQ-style triggers,' providing a clear use case. However, it does not explicitly state when not to use it or mention alternative tools for similar tasks, leaving some ambiguity.

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

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

No annotations, so description carries full burden. Discloses workspace creation, short-lived claim link, embed snippet, dashboard link, and that no reusable credentials are returned. Does not cover error scenarios or rate limits, but for a setup tool the key behaviors are well-explained.

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 paragraph with three sentences. It front-loads the main purpose, then explains two cases. Efficient, no unnecessary information, though slightly long for a simple tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Without an output schema, the description adequately describes return values (claim link, embed snippet, dashboard link) and side effects. Doesn't detail exact formats, but the agent can infer what to do with the results. Sufficient for a setup 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 coverage is 100% but description adds value by explaining that 'name' is ignored when workspace already exists. This clarifies conditional behavior beyond the schema's basic description.

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?

Clear verb 'Set up' and resource 'SingChat live-chat'. Distinguishes from siblings by stating 'Call this FIRST' and explaining the two distinct behaviors (new vs existing workspace). No ambiguity about what the tool accomplishes.

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 marks this as the first call to make, and describes when to use based on authentication state. Does not explicitly mention alternatives, but the context provided (new vs existing) guides appropriate use versus other tools like get_embed_snippet or claim_account.

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