Parsley - Buyer Intent Signals

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

With no annotations, the description should disclose behavioral traits like data aggregation, read-only nature, or permissions. It only states it provides an overview without clarifying if it's a static snapshot or computed dynamically.

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 that front-loads the key information. No extraneous text, but could be slightly more structured by listing metrics separately.

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 two parameters, the description should explain the return structure (e.g., format of counts, conversion rate). It omits aggregation details and possible filters beyond the parameters.

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

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

Schema description coverage is 50% (client param documented, days param not). The description does not explain what 'days' does (time range) or how 'client' modifies the query. It adds minimal 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 identifies this as a dashboard-level overview providing views, conversations, lead counts, and conversion rate. It distinguishes from sibling tools which target more specific data like individual conversations or hot leads.

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. The description implies it's for high-level metrics, but does not mention when to choose it over get_meddic_summary or get_hot_leads.

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 must fully disclose behavior. It only lists output data types but does not mention that the operation is read-only, whether any side effects exist, authentication needs, rate limits, or how to handle errors. The limited behavioral context leaves agents guessing.

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, efficiently conveying the tool's purpose and contents. However, it is a run-on list that could be broken into bullet points for clarity. It is not overly verbose.

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 2 parameters, no output schema, and no annotations, the description covers the return data types fairly well. However, it lacks details on pagination, error states, response format, or how to use the 'client' parameter effectively. It is sufficient for a simple fetch but not fully 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 coverage is 50% (only 'client' has a description). The description does not add meaning to the 'conversation_id' parameter, nor does it clarify the expected format or source. The mention of 'conversation' in the description aligns with the parameter but provides no semantic enrichment 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 retrieves 'full details of a single conversation' and enumerates specific data types (MEDDIC signals, engagement metrics, enrichment data, discovery field values). This distinguishes it from siblings like get_meddic_summary (likely narrower) and get_conversations (likely lists multiple 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 does not explicitly state when to use this tool versus alternatives like get_meddic_summary or get_conversations. It implies usage for a single conversation's full details but lacks when-not-to-use guidance or prerequisite conditions.

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 must disclose behavioral traits. It states 'list' (implying a safe read), but omits details like whether it's read-only, pagination behavior, what 'recent' means (default 30 days), or output format. Minimal transparency for a tool with no annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single concise sentence that front-loads the core purpose and key filters. No wasted words, though it could benefit from a structured breakdown of parameters. Efficient for a simple list tool.

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

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

Given 5 parameters, no annotations, no output schema, and many siblings, the description is too sparse. Missing defaults (days=30, limit=20), explanation of optionality (no required params), return format, and differentiation from search_by_intent. Does not provide enough context for confident use.

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 only 20% (only 'client' documented). The description adds meaning for three parameters (lead_quality, intent_signal, days) by naming them as filters, but fails to mention 'limit' and 'client' (important for agency use). Even for mentioned parameters, no further explanation of enum values or behavior. Does not compensate for low 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?

The description clearly states the verb 'list', the resource 'chatbot conversations', and specifies the available filters (lead quality, intent signal, date range). This distinguishes it from sibling tools like get_conversation_detail (detail) and get_hot_leads (leads).

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?

Usage is implied by the description: use when you need a list of conversations with those filters. However, no explicit guidance is given on when not to use it or alternatives. Agent must infer from sibling names; no exclusions stated.

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

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

With no annotations provided, the description carries full burden but only mentions that it gets leads with MEDDIC evidence. It does not disclose behavioral traits such as what constitutes 'hot' or 'warm', whether the operation is read-only, rate limits, or output format.

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 wasted words. The structure is ideal: first sentence states the core function, second provides 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?

Given the tool has 3 parameters with 33% schema coverage and no output schema, the description is insufficient. It lacks parameter explanations, output details, and any guidance on prerequisites or 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 description coverage is low (33%), so the description should compensate, but it adds no meaning beyond what the schema provides. It does not explain the 'days', 'client', or 'include_warm' parameters.

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 retrieves all hot and warm leads with MEDDIC evidence, and positions it as a morning briefing tool, which effectively distinguishes it from siblings like get_meddic_summary or get_lead_enrichment.

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 phrase 'The morning briefing tool' implies a use case, but it does not explicitly state when to use this tool versus alternatives like search_by_intent or get_meddic_summary, nor does it provide 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 are provided, and the description fails to disclose behavioral traits such as data volume, aggregation method, or whether results are filtered by the days parameter. The tool's behavior beyond the surface-level purpose is opaque.

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 concise and front-loaded with the main purpose. It is efficient but could be slightly expanded to add value without losing conciseness.

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 absence of an output schema and only partial parameter documentation, the description is insufficient. It does not explain return values, pagination, or grouping details, leaving significant gaps for an agent.

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

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

Schema description coverage is only 25%, with only the 'client' parameter having a description. The tool description does not add meaning to the other parameters (days, limit, topic) beyond their names, failing to compensate for low 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 surfaces unanswered questions from chatbot conversations and groups them by topic. This is a specific verb-resource combination that distinguishes it from sibling tools like get_conversation_detail or get_analytics_summary.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. The description does not specify context, prerequisites, or exclusions.

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

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

No annotations are provided, and the description does not disclose behavioral traits such as read-only nature, authentication requirements, or potential performance implications. For a tool with zero annotation coverage, the description carries the burden, but it adds no 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?

The description is a single, front-loaded sentence that efficiently conveys the tool's purpose. Every word earns its place, with no redundancy or fluff.

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

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

Given the absence of an output schema and annotations, the description is too brief to be complete. It does not explain the format of the results, any default behavior, or constraints (e.g., what if no enrichment data exists). The agent lacks sufficient context to reliably interpret the response.

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 only 33% (only the 'client' parameter has a description). The tool description does not explain the 'days' or 'has_company' parameters, nor does it add meaning beyond the schema. Since coverage is low, the description should compensate, but it fails to do so.

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 retrieves extracted company, role, timeline, and budget context from conversations, using a specific verb and resource. It is distinct from sibling tools like get_conversations (raw list) and get_meddic_summary (MEDDIC framework).

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. The context is implied by the description, but it does not state exclusions or prerequisites, which limits its helpfulness for an AI agent deciding between tools.

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 provides some behavioral context by stating it aggregates across all conversations, implying a read-only aggregate operation. However, it lacks details on permissions, rate limits, side effects, or whether results are filtered by user access.

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 gets to the point. It is front-loaded with the core action. It could be expanded slightly to add value without losing conciseness.

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 two parameters and no output schema, the description covers the basic purpose but leaves ambiguity about the output format, the meaning of 'MEDDIC signal distribution', and usage context relative to siblings. It is minimally valid but not fully informative.

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 does not add meaning to the parameters beyond the schema. Schema coverage is 50% (only 'client' has a description). The parameter 'days' lacks any description in schema or tool description, so the agent has no guidance on its role or format.

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 aggregates MEDDIC signal distributions across conversations. It uses a specific verb and resource, but does not explicitly differentiate from sibling tools like get_analytics_summary, which may overlap in function.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives. The description does not mention prerequisites, context, or situations where this tool is preferred over siblings like get_analytics_summary or get_conversations.

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 disclose all behavioral traits. It only mentions 'Find conversations,' which implies a read operation but does not confirm readOnly behavior, auth requirements, or any side effects. No details about pagination or result limits are given.

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 with no superfluous text. It is concise but lacks any structural elements like bullet points or examples. It could be slightly more informative without adding length.

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, no output schema, and no annotations, the description is insufficient. It does not explain return format, ordering, pagination, or how parameters like 'days' affect results. The tool is moderately complex, and the description fails to provide enough context for correct invocation.

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 mentions three of the five parameters (MEDDIC signals, intent score, topic) but omits 'days' and 'client'. Schema coverage is only 20%, so description partially compensates. The 'client' parameter has a schema description, but the tool description does not repeat it. Parameters with enums are absent, so no enum guidance is needed.

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

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

The description states it finds conversations by MEDDIC signals, intent score, or topic. This clearly indicates a filtering/search capability, distinguishing it from generic list tools like get_conversations. However, it does not explicitly contrast with siblings like get_meddic_summary, which might also use MEDDIC data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, context, or when not to use it. The agent must infer usage from the name and description alone.

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