Query buyer intent signals, MEDDIC qualifications, and lead scores from Parsley.
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Average 3.3/5 across 8 of 8 tools scored.
Most tools are clearly distinct, but get_conversations and search_by_intent both return conversations, and get_hot_leads vs get_lead_enrichment have some overlap. Descriptions clarify their specific purposes, so ambiguity is minimal.
The majority of tools follow the get_* pattern, but search_by_intent deviates. Otherwise, naming is consistent and readable.
With 8 tools, the count is well-scoped for the server's purpose. Each tool focuses on a distinct aspect of buyer intent, from summaries to specific searches, without unnecessary bloat.
The server covers core needs: overview, listing, detail, enrichment, MEDDIC summary, knowledge gaps, and search. Missing are non-read operations and a general lead list (only hot leads), but these are minor gaps for an analytics-focused server.
get_analytics_summaryGet analytics summaryCDashboard-level overview: views, conversations, lead counts, conversion rate.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | ||
| client | No | Agency only: target a managed client org by its id to query that client's data. Omit for your own. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only behavior, but the description adds little beyond the list of metrics. It does not explain how the data is aggregated, whether the overview is limited to a date range, how the client parameter affects the result, or any other behavioral detail. The description is essentially a summary of contents, not behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that immediately states what the tool provides. There is no unnecessary wordiness, and the metrics are listed in a compact, scannable way.
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 optional parameters, the description gives basic metric names but omits return format, aggregation level, and parameter effects. The absence of an output schema places more burden on the description to explain what the agent will receive, which it does not do.
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 50% schema description coverage, only the 'client' parameter has an explanation in the schema. The description does not clarify the 'days' parameter or how it interacts with the summary. Since the description adds no parameter-specific information, it fails to compensate for the missing schema 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?
The description clearly states the tool provides a 'dashboard-level overview' of key metrics (views, conversations, leads, conversion rate), which distinguishes it from sibling tools that focus on specific details. However, it lacks an explicit verb and could be more precise about the exact scope, so it doesn't reach a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention any exclusions, prerequisites, or context for choosing this over siblings like get_conversations or get_hot_leads. The 'overview' label implies a general use case, but no explicit direction is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_conversation_detailGet conversation detailAGet full details of a single conversation: MEDDIC signals, engagement metrics, enrichment data, and any owner-configured discovery field values captured during the chat (e.g. company size, current tooling, hiring status).
| Name | Required | Description | Default |
|---|---|---|---|
| client | No | Agency only: target a managed client org by its id to query that client's data. Omit for your own. | |
| conversation_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate a safe read-only operation (readOnlyHint=true). The description adds meaningful context about the scope of data returned, including owner-configured discovery fields, which goes beyond the simple 'get details' phrasing. No contradictions with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that immediately states the tool's purpose and then elaborates with concrete examples. Every phrase adds value, with no redundancy or filler.
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-resource read tool with good annotations and a clear parameter schema, the description sufficiently covers what data is returned. It does not explain error conditions or rate limits, but given the simplicity of the operation, these are not critical for basic 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?
Schema description coverage is only 50% (client is described, conversation_id is not). The description does not clarify what conversation_id should look like or how to obtain it, and does not compensate for the undocumented required parameter. The mention of 'single conversation' is the only hint.
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 retrieves full details of a single conversation, listing specific content categories (MEDDIC signals, engagement metrics, enrichment data, discovery fields). This distinguishes it from sibling tools like get_conversations (list) and get_meddic_summary (summary only).
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 use when you need detailed data for one specific conversation, contrasting with listing tools. However, it provides no explicit when-not-to-use or alternative tool references, so usage guidance is only implied rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_conversationsList conversationsBList recent chatbot conversations with filtering by lead quality, intent signal, and date range.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | ||
| limit | No | ||
| client | No | Agency only: target a managed client org by its id to query that client's data. Omit for your own. | |
| lead_quality | No | ||
| intent_signal | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and openWorldHint=false, and the description consistently says 'List', so no contradiction. However, it adds little behavioral context beyond mentioning filtering capabilities, which are already visible in the schema. No information on pagination, rate limits, or results structure.
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?
One sentence, no fluff, front-loaded with the action. It earns its place but with no extra waste.
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 there are 5 parameters, no output schema, and no usage guidance, the description is insufficient for an agent to invoke this tool correctly in all cases. It omits the client parameter, pagination, and result format.
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 only 20%; only 'client' has a description. The description mentions filtering by lead quality, intent signal, and date range, corresponding to lead_quality, intent_signal, and days, but provides no detail on accepted values (enums), limits, or the agency-specific client parameter. It doesn't compensate for the 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 tool lists recent chatbot conversations and supports filtering by lead quality, intent signal, and date range. The verb 'List' and resource 'chatbot conversations' are specific, but it doesn't explicitly distinguish from sibling tools like get_conversation_detail or search_by_intent.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives such as get_hot_leads or search_by_intent. There are no exclusions or contextual hints, so the agent is left to infer usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_hot_leadsGet hot leadsAGet all hot and warm leads with MEDDIC evidence. The morning briefing tool.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | ||
| client | No | Agency only: target a managed client org by its id to query that client's data. Omit for your own. | |
| include_warm | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already declares this a safe read operation, so the description doesn't need to restate that. It adds context about the scope (all hot and warm leads) and the MEDDIC evidence filter, but does not disclose behaviors like pagination, ordering, or data freshness. With annotations covering the safety profile, the added context is sufficient but not rich.
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 and front-loaded: the first sentence delivers the core action and scope, the second adds a memorable use-case tagline. 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?
The description gives the main purpose but omits details about output format and parameter effects. With no output schema and modest parameter documentation, a more complete description would help. The 'MEDDIC evidence' and 'morning briefing' cues provide some context about the nature of the 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 description coverage is only 33%, and the description does not mention any parameters. It provides no clarification for 'days' or 'include_warm', leaving their semantics ambiguous. The phrase 'hot and warm leads' hints at include_warm, but the description fails to compensate for the 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 verb ('Get') and the resource ('all hot and warm leads'), and specifies the unique qualifier 'with MEDDIC evidence', which distinguishes it from sibling tools like get_lead_enrichment or get_meddic_summary. The 'morning briefing' tagline further narrows its intended role.
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 via 'The morning briefing tool' but does not explicitly state when to use it over alternatives, nor does it mention exclusions or scenarios where siblings should be preferred. This is implied context rather than clear guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_knowledge_gapsGet knowledge gapsASurface unanswered questions from chatbot conversations, grouped by topic.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | ||
| limit | No | ||
| topic | No | ||
| client | No | Agency only: target a managed client org by its id to query that client's data. Omit for your own. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint=true and openWorldHint=false, so safety and world scope are covered. The description adds a behavioral trait: results are grouped by topic. However, it does not disclose details like aggregation windows, whether results are global or per-client, or any filtering effects beyond topic.
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 immediately states the tool's purpose. Every word 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?
The tool is low complexity with no required parameters and a read-only hint, but without an output schema the description does not explain what the returned data looks like or how parameters like 'days' and 'limit' affect results. It is minimally sufficient for selection but not for fully 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 input schema has only 25% description coverage (only 'client' is described). The description mentions 'grouped by topic', which gives partial meaning to the 'topic' parameter, but it does not explain 'days' or 'limit' semantics beyond their schema types and defaults. Since schema coverage is below 50%, the description should compensate but does not.
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 with a specific verb ('surface'), resource ('unanswered questions from chatbot conversations'), and grouping behavior ('grouped by topic'). It uniquely distinguishes itself from sibling tools like get_conversations or get_analytics_summary by focusing on knowledge gaps.
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 when you want to discover unanswered questions from chatbot conversations, but it does not explicitly state when to use this tool instead of alternatives, nor does it mention any exclusions or prerequisites. There is no reference to sibling tools or use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_lead_enrichmentGet lead enrichmentBGet extracted company, role, timeline, and budget context from conversations.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | ||
| client | No | Agency only: target a managed client org by its id to query that client's data. Omit for your own. | |
| has_company | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true, and the description's 'Get' aligns with that. The description adds minimal behavioral context (e.g., source is conversations) but does not disclose filtering behavior, time windows, or output structure. It is not contradictory, but adds only marginal value beyond 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 with no padding or repetition. It front-loads the core action and result, earning a high score for efficiency.
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 read-only tool with optional filters, the description is adequate but lacks important context like default behavior (days=30), how has_company affects results, and what 'enrichment' means in practice. The schema helps, but the description doesn't fully round out the tool's 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 schema covers only 33% of parameters with descriptions (only 'client' has one), and the description does not explain 'days' or 'has_company' at all. It adds no meaning beyond what the schema already provides, failing to compensate for the 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 it retrieves extracted company, role, timeline, and budget context from conversations, which is a specific verb+resource combo. It distinguishes itself from sibling tools like get_hot_leads or get_meddic_summary by focusing on enrichment context.
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 the listed sibling tools. It only states what it does, without mentioning use cases, exclusions, or alternatives, leaving the agent without explicit selection criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_meddic_summaryGet MEDDIC summaryBAggregate MEDDIC signal distribution across all conversations.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | ||
| client | No | Agency only: target a managed client org by its id to query that client's data. Omit for your own. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the agent knows this is a safe read operation. The description adds minimal extra behavior by indicating it aggregates/summarizes rather than returns raw conversations, but does not disclose return format, performance considerations, or other side effects. This is adequate but not rich.
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, direct sentence with no filler. It states the core action and scope concisely, earning its place 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?
Given the tool is simple and read-only with two optional parameters, the description gives the core idea ('aggregate MEDDIC signal distribution') but is incomplete without an output schema or details on what form the distribution takes. It lacks information about the return value and any caveats, but the tool's simplicity and read-only annotation keep it at a minimum viable level.
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% (only the 'client' parameter has a description; 'days' has none). The tool description does not compensate by explaining either parameter, and the meaning of 'days' (e.g., a lookback window) is left ambiguous. Since coverage is not high and the description adds no param semantics, the score is low.
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 'Aggregate MEDDIC signal distribution across all conversations' uses a specific verb (aggregate), identifies the resource (MEDDIC signal distribution), and defines scope (across all conversations). This clearly distinguishes it from siblings like get_conversation_detail (individual conversations) and get_analytics_summary (generic analytics).
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 mention any exclusions, prerequisites, or contrasting tools. The usage is only implicit from the name and scope, which does not meet the bar for explicit context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_by_intentSearch by intentCFind conversations matching specific MEDDIC signals, intent score, or topic.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | ||
| topic | No | ||
| client | No | Agency only: target a managed client org by its id to query that client's data. Omit for your own. | |
| meddic_signals | No | ||
| min_intent_score | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=false, so the agent knows this is a safe read operation. However, the description adds no behavioral context such as pagination, result limits, output format, or how the search is executed, which would be useful beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence with no redundant words. It front-loads the main purpose and does not waste the agent's attention on filler. It is appropriately sized for a straightforward search 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?
With 5 optional parameters, no output schema, and low schema description coverage, the description leaves the agent under-informed. It does not explain what the tool returns, how results are ordered, or how the search handles missing criteria. The single sentence is insufficient for an agent to fully anticipate the tool's behavior in all 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 only 20% (only 'client' is described). The description mentions MEDDIC signals, intent score, and topic, but does not explain the meaning of these parameters, the valid enum values for meddic_signals, or the range of min_intent_score. It also omits the 'days' parameter entirely, leaving a significant semantic gap.
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 finds conversations by MEDDIC signals, intent score, or topic, which is a specific verb+resource+scope. It distinguishes itself from the general get_conversations sibling by implying an intent-based search, though it does not explicitly name the alternative.
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?
There is no explicit guidance on when to use this tool versus alternatives like get_conversations or get_meddic_summary. The usage is only implied by the tool name and description, but no exclusions or alternative recommendations are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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