MisarReach MCP Server

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

Since no annotations are provided, the description carries the burden of behavioral disclosure. It correctly identifies the operation as a creation ('Create a new deal'), which implies mutation. However, it does not mention any prerequisites (e.g., whether the lead email must already exist in the system), side effects (e.g., whether a notification is sent), or required permissions, leaving some behavioral aspects unclear.

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 communicates the tool's core action and key constraint. Every word earns its place, with no redundancy or wasted text.

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 8 parameters, no output schema, and no annotations, the description is somewhat sparse. It clearly states the purpose but does not explain the output (e.g., the returned deal ID) or provide any usage guidance for the many parameters. While the schema covers parameter descriptions, the description alone does not make the tool fully self-contained for an AI agent, especially for complex parameters like 'value' and 'currency'.

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 all 8 parameters have descriptions in the schema. The tool description itself does not add any parameter-specific meaning beyond what is already in the schema (it only mentions 'linked to a lead email'). Thus, the description provides no additional value for parameter semantics, and the baseline score of 3 is appropriate.

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

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

The description uses a specific verb ('Create') and resource ('a new deal') while clearly specifying the linking constraint ('linked to a lead email'). This distinguishes it from siblings like 'update_deal' and 'move_deal_stage', which modify existing deals, and from 'create_lead_list', which deals with a different resource.

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 this tool is for creating a new deal that is associated with a lead email, providing clear context. However, it does not explicitly state when not to use this tool (e.g., if the lead email does not exist) or suggest alternative tools like 'update_deal' for modifying existing deals, missing a slight opportunity for 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?

The description simply says 'Create a new lead list' without disclosing any behavioral details. Since no annotations are provided to clarify safety or side effects (e.g., read/write/destructive hints), the description must convey this. It does not mention whether creation overwrites an existing list, requires specific permissions, triggers side effects, or has 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?

The description is a single short sentence, which is efficient and front-loaded. It contains no filler or redundant information, but could perhaps be slightly more specific (e.g., noting that names have a length constraint).

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 that the tool has only one parameter and no output schema, the description is minimal but incomplete. It fails to mention what the tool returns upon success, any constraints on the list (e.g., uniqueness, character limits beyond what the schema says), or any side effects (e.g., whether an existing list with the same name is replaced).

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 is small (1 parameter with 100% coverage) and the description does not add extra context beyond 'name' being a required string. With complete schema coverage, a baseline of 3 is appropriate because the schema already explains the parameter adequately.

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 'Create a new Hunter.io lead list.' clearly states the verb ('Create') and the resource ('lead list'), and it distinguishes the tool from its siblings because 'create_lead_list' is the only creation operation in the sibling list. It is specific and unambiguous.

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 when the list should be created or how it interacts with other lead-related tools like 'list_lead_lists' or 'sync_lead_list'. No exclusions or prerequisites are mentioned.

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

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

With no annotations, the description must disclose behavioral traits fully. It mentions 'via Hunter.io' (external dependency) and optional email fetching, but does not state read-only nature, rate limits, data freshness, or if results are paginated. For a tool with 8 parameters, more behavioral context is needed.

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, front-loading the main action and optional feature. Every word is necessary; no redundancy. Perfectly concise for the core idea.

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?

Despite having 8 parameters and no output schema, the description does not explain return format, pagination, error handling, or how email fetching impacts output. An agent would lack key context for robust 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?

Schema description coverage is 100%, so baseline is 3. The description adds no parameter-specific meaning beyond what the schema already provides (e.g., 'matching criteria' is generic). No additional value from the 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 discovers companies via Hunter.io using criteria, and optionally fetches emails. The verb 'discover' and resource 'companies' are specific, and the tool is well-differentiated from sibling tools like search_leads or enrich_lead, which focus on existing 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?

The description implies usage for finding companies, but provides no explicit guidance on when to use this tool versus alternatives (e.g., search_leads) or conditions like required prerequisites. The context is clear but lacks exclusion or comparison.

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 full burden. It discloses credit consumption but does not clarify whether enrichment is a mutation (updating the lead) or returns data, nor does it cover idempotency, error handling, or response format. The behavioral profile remains vague for an AI agent.

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?

Only two sentences: the first clearly states the action and included data types, the second adds the cost constraint. No wasted words; front-loaded with essential purpose.

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

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

For a simple tool with one parameter and no output schema, the description should explain what happens after enrichment (e.g., lead is updated, returns enriched data). It does not, leaving gaps in expected behavior. The credit mention is helpful but insufficient for full 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 description coverage is 100% — the leadId parameter already has a clear description ('Lead UUID from list_leads'). The tool description adds no additional meaning to the parameter beyond repeating the credit cost. Baseline 3 is appropriate.

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

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

The description uses a specific verb ('Enrich') and resource ('a lead'), and lists exact data fields (seniority, department, LinkedIn, phone, company size, industry). It clearly distinguishes this tool from siblings like 'score_leads' or 'verify_emails' by specifying the enrichment 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?

The description implies usage when you need person/company data on a lead, but does not provide explicit when-to-use or when-not-to-use guidance relative to list_leads, search_leads, or score_leads. It mentions credit consumption as a constraint but lacks prerequisites or alternative 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, so the description carries the full burden. It states 'Get the current status and progress' which implies a read-only, non-destructive operation, but it does not disclose whether repeated calls are safe, if any side effects exist, authentication requirements, or what the return format is (e.g., a string, object with status codes). The description is too brief to fully inform an agent of behavioral traits beyond the obvious read action.

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, which is concise and front-loaded. However, it sacrifices completeness for brevity. While there is no wasted text, the tool context is simple enough that more detail could have been added without harming conciseness. It is adequate but not exemplary.

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

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

Given the tool has no output schema and only one parameter, the description should explain what 'status and progress' means (e.g., possible statuses, percentage progress). Without that, an agent cannot anticipate the return value or use the result effectively. The description feels incomplete for an agent to reliably invoke and process 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 coverage is 100% for the single parameter 'runId' with description 'Autopilot run UUID'. The description adds no extra meaning beyond what the schema already provides. Baseline of 3 is appropriate since the schema carries the burden adequately; the description does not enrich the parameter understanding.

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 ('Get') and the resource ('status and progress of a specific autopilot run'). It effectively distinguishes from sibling tools like 'list_autopilot_runs' (lists all runs) and 'start_autopilot' (initiates a run) by focusing on a single run's current state.

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 given on when to use this tool versus alternatives. For example, it does not clarify that this is for fetching the status of a particular run while 'list_autopilot_runs' is for finding the run ID or seeing all runs. No when-to-use or when-not-to-use information is present.

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

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

No annotations are provided, so the description carries the full burden. It states the tool returns status, configuration, and delivery stats, but does not disclose any behavioral traits such as read-only nature, authentication requirements, rate limits, or data freshness. It is straightforward 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?

A single sentence of 14 words that is front-loaded with the key action. Every word earns its place; no wasted text.

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 parameters, no output schema, and no annotations, the description is minimally adequate. It lists the information returned (status, configuration, delivery stats) but lacks details on return structure, error cases, or whether all channels are always included. Could be more complete for a simple tool.

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

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

The input schema has no parameters and schema description coverage is 100%. The description adds no parameter information since none exist. Baseline score of 4 is appropriate as the description does not need to compensate for missing parameter details.

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

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

The description clearly states the action ('Get') and the resource ('status, configuration, and delivery stats for all outreach channels') with specific channel examples. It distinguishes itself from sibling tools like 'update_channel' and 'get_pipeline' by focusing on channel status.

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 (when you need channel status), but there is no mention of when not to use it or what other tools might be better for specific scenarios.

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

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

No annotations are provided, so the description carries the full burden. It discloses the output content (grouped deals with revenue) but does not mention behavioral traits such as read-only nature, authentication requirements, rate limits, or whether the response is paginated or sorted. The description is adequate 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 a single, well-structured sentence of 18 words. It is front-loaded with the action and resource, then provides specifics about stages and revenue. No redundant or 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 one-parameter tool with no output schema, the description covers the core functionality and output. However, it does not detail the return structure (e.g., whether stages are keys, how revenue totals are formatted), leaving some ambiguity. It is adequate but not exhaustive.

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 description coverage is 100% (one parameter with a clear description). The tool description adds no additional meaning or context about the 'workspaceId' parameter beyond what the schema already provides. Baseline 3 is appropriate per the guidelines.

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

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

The description uses a specific verb ('Get') and resource ('full sales pipeline board'), explicitly listing the deal stages and mentioning revenue totals. This clearly distinguishes it from sibling tools like 'list_deals' (which likely returns flat deals) and 'move_deal_stage' (which modifies stages).

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 the tool is for retrieving a grouped pipeline overview, but it does not explicitly state when to use it versus alternatives (e.g., 'list_deals' for individual deals, 'get_sales_agent_actions' for agent activity). No when-not-to-use or prerequisite 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?

No annotations provided, so description bears full burden. Discloses the basic behavior (fetch today's data) and output categories, but does not mention side effects, permissions, data freshness, or what happens if no actions exist. Adequate 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?

Single sentence, concise, and front-loaded with the core action. Every word earns its place. No redundant or vague phrasing.

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

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

Given the tool has no parameters and no output schema, the description provides the essential context: it returns today's actions with summary stats. For a simple read-only tool, this is nearly complete. Could be slightly improved by explicitly noting the 'today only' scope and a hint about output format, but adequate as is.

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?

Input schema has 0 parameters, baseline is 4. The description adds meaning by specifying the temporal scope ('today's') and the type of data (actions + summary stats), which the empty schema does not convey.

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 identifies the resource ('today's AI sales agent actions') and the action ('Fetch'). Lists specific summary stats (total actions, deals closed, flagged for human review), distinguishing it from sibling tools like get_autopilot_status or get_pipeline.

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 vs alternatives. Does not mention limitations (e.g., only today's data), prerequisites, or when not to use it. Many sibling tools exist, and this description offers no decision context.

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

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

With no annotations provided, the description carries the full burden. It accurately states it's a read operation with no side effects, but does not disclose other behavioral traits such as caching behavior, error conditions, or the structure of the config (which is unknown without output schema).

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 filler. Every word adds value, clearly stating the action and target 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?

Given there is no output schema and no annotations, the description does enough to let an agent know it retrieves a configuration, but lacks detail on the config's structure or potential variations. Contextual completeness is adequate for a simple fetch but not rich.

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

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

The tool has zero parameters and schema coverage is 100%, so the description correctly adds no parameter details. It provides a precise semantic context for what the tool returns (user's AI sales agent config). The description adds meaning beyond the empty 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 'Fetch' and the resource 'current user's AI sales agent configuration', distinguishing it as a read operation. However, it does not explicitly differentiate from the sibling tool 'update_sales_agent_config', though the verb contrast is implied.

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 like 'update_sales_agent_config' or other config-related tools. The description lacks context on prerequisites or scenarios.

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

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

No annotations are provided, so the description carries the full burden. It clarifies that the tool is used for polling, which implies it may be called repeatedly to check job completion. However, it doesn't disclose whether the job is asynchronous, run durations, or error states.

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?

Extremely concise at one sentence. No fluff, but also leaves out useful detail.

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 polling tool with no output schema, the description should explain what the result contains (e.g., statuses, result data format). The minimalism leaves comprehensive understanding to the AI, risking incorrect 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?

With 100% schema coverage and only one required parameter, the description adds little beyond what the schema provides. At best, it reinforces the purpose of the jobId.

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 what the tool does: poll status and results of a lead search job. However, it is somewhat generic and doesn't differentiate between this and other search or status tools in the sibling list.

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 guidance on when to use this tool over alternatives like 'search_leads' or 'list_leads'. The description implies usage for polling, but doesn't specify prerequisites, polling frequency, or indications for use.

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

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

With no annotations, the description must convey behavioral traits. It only states the tool lists runs with status and summary, but omits whether it is read-only, how results are ordered, pagination behavior, or authentication requirements. The agent cannot infer safety or side effects.

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 key information. While it could be slightly more detailed, it avoids unnecessary repetition and is well-structured 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?

The description does not explain the return format, default limit, ordering, or how to interpret 'status' and 'summary results'. It also fails to differentiate from sibling tools or mention typical use cases. Given no output schema and 27 siblings, more context is needed.

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 each parameter documented in the input schema. The description adds no additional context beyond what the schema already provides, so baseline score of 3 is appropriate.

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

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

The description clearly states the action ('List'), the resource ('past autopilot runs'), and the output ('status and summary results'). This distinguishes it from siblings like 'get_autopilot_status' which targets a single run.

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 such as 'get_autopilot_status' or 'get_sales_agent_actions'. The description lacks context about prerequisites, default parameters, or complementary 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?

There are no annotations provided, so the description must fully disclose behavioral traits. While it mentions an optional status filter, it does not state whether results are sorted (e.g., by date or name), if the tool is read-only, or any rate limits. The description lacks detail on how the revenue summary is computed (e.g., based on filtered deals vs. all deals).

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 very concise, consisting of two short sentences that front-load the core purpose (list deals with optional filter) and immediately describe the return value. 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?

The tool has 3 optional parameters, no output schema, and no annotations. The description covers the basic purpose and return structure but lacks details on sorting, error cases, or behavior with edge cases (e.g., empty results, large offsets). Given the moderate complexity, the description is minimally adequate but leaves gaps.

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

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

Schema description coverage is 100% (all three parameters have descriptions in the schema). The description adds value by clarifying the return structure (deals array + revenue summary), which is not in the schema. However, it does not elaborate on the status parameter values 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?

The description clearly states the tool lists deals with an optional status filter and describes the return structure (deals array + revenue summary). This effectively differentiates it from siblings like list_lead_lists, list_leads, and list_autopilot_runs.

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 provide guidance on when to use this tool versus alternatives. For example, there is no mention of when to prefer list_deals over get_pipeline or move_deal_stage, nor when not to use it. No when-not-to or alternative suggestions are 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?

With no annotations, the description carries full behavioral burden but only states it is a list operation. It does not disclose pagination behavior, ordering, rate limits, or authentication requirements, leaving the agent with minimal behavioral insight beyond the tool name.

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 with no wasted words. Front-loaded with the verb and resource. Every word earns its place.

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

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

Given no output schema and no annotations, the description is minimal. It covers the basic purpose but doesn't explain return value format, potential limits, or how results are organized. Adequate for a simple list operation, but could be more 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?

No parameters exist in the input schema, so schema coverage is effectively 100%. The description correctly implies no additional parameters are needed. Baseline for zero parameters is 4, and the description adds no unnecessary clutter.

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 action (List), resource (lead lists), and scope (associated with the account). Differentiates from siblings like 'list_leads' by specifying 'lead lists' as the resource.

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 like 'list_leads' or 'sync_lead_list'. No mention of prerequisites or context for invoking it.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does not mention the tool's idempotency, rate limits, or any destructive potential, which is a gap for a read operation. However, the description's mention of pagination and filtering is helpful.

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

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

The description is a single, clear sentence that covers the main action and key features. It is front-loaded and avoids unnecessary words, making it easy to scan.

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 complexity of the tool (4 params, pagination, search) and the lack of output schema or annotations, the description is minimally adequate but could be more complete. It does not describe the return format, default ordering, or behavior when no results are found.

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 input schema already documents all parameters well. The description adds minimal value beyond 'supports pagination and search', but does not re-explain or clarify parameter details. Baseline 3 is appropriate.

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

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

The description clearly states it lists saved leads for the authenticated user. However, it does not distinguish itself from sibling tools like 'list_lead_lists' or 'search_leads', which could cause 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?

The description implies when to use the tool by mentioning pagination and search, but it lacks explicit guidance on when to use this tool over alternatives like 'search_leads' or 'list_lead_lists'. No exclusions or prerequisites are 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?

No annotations are provided, so the description must fully disclose behavioral traits. It only describes the action ('move') and its scope ('pipeline stage'), but fails to state whether this might trigger automation rules, cascade to other records, require specific permissions, or have immutability constraints. The 'drag-and-drop equivalent' hint is weak for behavioral transparency.

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 wasted words. It efficiently communicates the core action and a helpful analogy. Every word earns its place.

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

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

Given the lack of annotations and output schema, the description should compensate by clarifying constraints, side effects, or return behavior. It does none of these. For a simple two-parameter tool, the description is adequate for basic selection but insufficient for safe invocation, especially without knowing if the operation is reversible or logs changes.

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 the description does not need to elaborate on parameters. The description itself adds no additional meaning beyond the schema, only restating the action. Baseline 3 is appropriate since the schema already documents the two parameters fully, including the enum values for 'newStage'.

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 a clear action ('move a deal') and a specific resource ('pipeline stage'). The 'drag-and-drop equivalent' provides useful real-world analogy. However, it doesn't explicitly differentiate from sibling tools like 'update_deal' which might also modify a deal's properties, though the stage-change focus is strong enough.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'update_deal' or other pipeline-related tools. There is no mention of prerequisites (e.g., deal must exist in a pipeline), nor any indication of when not to use it. The agent must infer usage context from the tool name alone.

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

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

With no annotations provided, the description must fully disclose behavioral traits. It clearly states this is a generative preview tool (non-destructive, read-like) and emphasizes 'public access — no auth required,' which is critical for an AI agent to understand security implications. The description doesn't cover rate limits, output size, or persistence aspects, but for a simple preview endpoint the transparency is largely 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?

The description is just two sentences, each carrying distinct value: the first defines the tool's core purpose, the second provides essential access information. No filler, no redundancy. Perfectly sized for an AI agent to quickly understand without unnecessary detail.

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, no nested objects), the description is mostly complete: it explains what the tool does, that it's a public preview, and the parameters are self-evident from the schema. It could benefit from mentioning that the output is a text message and possibly that it's for preview only (not for sending). However, for a small-scope tool with excellent schema coverage, this is highly adequate.

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

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

Schema coverage is 100%, and the description does not add parameter-level details beyond what the schema already provides (e.g., name is full name 1-100 chars, role optional, company optional). The description implies using the name for personalization but doesn't elaborate on how role or company influence the output. Since schema coverage is high, 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 uses specific verbs ('Generate a sample') and a clear resource ('AI-personalised outreach message for a person'), immediately distinguishing it from sibling tools like 'create_deal' or 'enrich_lead' which serve different purposes. The additional note about being a public endpoint with no auth reinforces its unique usage scenario.

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 it is a 'Public endpoint — no auth required,' clearly indicating when this tool is appropriate (quick previews without authentication). However, it does not explicitly mention when not to use it or contrast with alternatives like 'enrich_lead' or 'search_leads' which might provide more detailed data. Missing explicit exclusion cases.

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 mentions logging to the agent activity feed, but does not state whether the tool is idempotent, whether it can be called multiple times on the same conversation, or what happens if the conversation is already being processed. It also does not clarify if the action is always logged even if no reply is generated. For a pipeline tool that presumably mutates state, more transparency is needed.

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 and front-loaded with the key action ('Run the AI sales agent pipeline'). It is efficient and avoids fluff. However, it could be slightly more structured by separating the primary action from the logging side effect, but overall it is concise and well-organized.

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 that there is no output schema, the description should explain what the tool returns (e.g., does it return the decided action? the reply? a success indication?) but does not. The tool has one parameter, so complexity is low, but the lack of output description and behavioral details makes it incomplete. The strong sibling context adds pressure to differentiate, which the description partially achieves but not fully.

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 only one parameter, so the baseline is 3. The description adds no meaning beyond the schema: it repeats the idea of processing a conversation but does not explain what a valid 'conversationId' looks like or mention any constraints. Since the schema already documents the parameter well, the description does not add value here, but it does not contradict the schema either.

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 runs a sales agent pipeline on a conversation and decides the next action, optionally generating a reply. It uses a specific verb ('Run') and resource ('AI sales agent pipeline'), which distinguishes it from other tools that might handle static data or manual updates.

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 guidance on when to use this tool versus alternatives. With many sibling tools like 'get_sales_agent_actions' or 'update_sales_agent_config', the description does not specify prerequisites (e.g., does the conversation need to be active?) or exclusions (e.g., do not use if the agent is already processing). The lack of when-to-use or when-not-to-use guidance makes it harder for an agent to select this tool correctly.

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

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

No annotations are provided, so the description carries full burden. It discloses that the operation runs in background, returns immediately with a count, and consumes AI credits. However, it does not detail error handling, idempotency, permissions, or how to access results after triggering.

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

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

Three concise sentences with front-loaded purpose and two usage modes. The second sentence adds behavioral context (background, return count), and the third notes credit consumption. Every sentence adds value, though could potentially be more structured with bullet points for clarity.

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 2 parameters, no output schema, and no annotations, the description covers key aspects: purpose, modes, async behavior, and resource cost. However, it lacks details on how to retrieve scoring results, error scenarios, what happens if both parameters are provided, and whether the operation is idempotent. These gaps reduce completeness for a trigger tool.

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

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

The input schema has 100% coverage for both parameters. The description reinforces the two usage modes and the mutual exclusivity of jobId vs leadIds, but adds no new parameter-level information beyond what the schema already provides (including the max 200 limit on leadIds). Baseline of 3 is appropriate given full 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 triggers AI scoring for leads, with two specific modes: all unscored leads in a job or specific lead IDs. It uses a specific verb ('trigger') and resource ('AI scoring for leads'), distinguishing it from siblings like 'enrich_lead' which handle enrichment, not scoring.

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 the tool (for on-demand scoring) but does not provide explicit guidance on when not to use it or how it compares to alternatives like 'enrich_lead' or other lead processing tools. The mention of 'all unscored leads' implies a batch process, but no exclusions or prerequisites are 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 the full burden. It discloses key behavioral traits: the tool is asynchronous (returns jobId, poll for results), AI-powered, and consumes search credits. This is sufficient transparency, though it could mention limits or cancellation.

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: three sentences, front-loaded with the purpose, followed by the async behavior and credit cost. Every sentence adds value, and there is 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?

Given the tool's complexity (3 params, nested object, async, credit consumption, no output schema), the description is fairly complete. It covers the core action, how to get results, and a cost. It does not explain the query's role or the AI enrichment, but the schema covers parameters. The polling instruction is a key completeness element.

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 baseline is 3. The description does not add any additional meaning to the parameters beyond what the schema already provides. It does not mention query, useAI, or filters, leaving the schema to fully define them.

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 starts an AI-powered lead search job and returns a jobId. It distinguishes from the sibling get_search_job_status by mentioning polling, and from other tools like discover_companies or enrich_lead by focusing on starting a search job. However, it does not explicitly differentiate from similar search tools like discover_companies.

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 a usage guideline: poll get_search_job_status for results. It also mentions credit consumption as a constraint. However, it does not discuss when to use this tool versus alternatives like discover_companies or enrich_lead, nor does it specify prerequisites or conditions for use.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It discloses that it's a bulk import operation and that leads must belong to the authenticated user, but it does not mention whether the operation is idempotent, what happens if a lead already exists in the list, whether the campaign must exist, or any rate limits or side effects. This is minimal transparency.

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

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

The description is extremely concise (two sentences, 13 words) and front-loaded: the first sentence states the core purpose immediately. No wasted words or redundant information.

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

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

The tool has 3 parameters, no output schema, and no annotations. The description does not mention what the tool returns, whether it is synchronous, or any error conditions. For a mutation tool, this leaves significant gaps in understanding the full behavior and expected 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 100%; each parameter is already described in the schema. The tool description adds no additional meaning beyond the schema (e.g., it does not explain the relationship between listId and campaignId or the default behavior). With high schema coverage, baseline 3 is appropriate.

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

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

The description clearly states the action ('bulk-import selected lead IDs'), the target resource ('into a campaign's contact list'), and the scope (selected leads, must belong to authenticated user). This distinguishes it from sibling tools like sync_lead_list or create_lead_list, which have different purposes.

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

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

The description implies usage when you need to add lead IDs to a campaign's contact list, and mentions a constraint (leads must belong to authenticated user). However, it provides no explicit guidance on when to use this tool versus alternatives (e.g., sync_lead_list), nor does it state when not to use it.

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

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

No annotations are provided, so the description carries full burden. It clearly states the tool is 'fire-and-forget' (non-blocking), that it consumes 'autopilot credits', and that progress is tracked via polling. This gives useful behavioral context beyond the schema.

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, each adding a unique piece of information: purpose, fire-and-forget nature, return value and next step, and side effect. 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?

The tool has 2 params (100% schema coverage), no output schema, and no annotations. The description explains the process flow and a side effect (credits). It could mention that the runId is the primary output, but the rest is adequate for a simple initiate-and-poll pattern.

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

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

Schema description coverage is 100%, so baseline is 3. The description mentions 'Provide a goal' and the goal constraint '10-500 chars' is already in the schema; it adds minimal extra context. The workspace_id is not elaborated further.

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 'Start an AI autopilot run for outreach automation' with a specific verb ('start') and resource ('autopilot run'). It distinguishes this from siblings like 'get_autopilot_status' and 'list_autopilot_runs' by describing it as a fire-and-forget initiation.

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 says 'fire-and-forget' and mentions to 'poll get_autopilot_status for progress', which implies one-time use for initiating a run. It also indicates credit consumption, but does not explicitly state when not to use it or name alternatives for different scenarios.

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

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

No annotations are provided, so the description carries full burden for behavioral disclosure. It only says feedback improves future quality, but does not disclose idempotency, rate limits, permission requirements, or what happens on duplicate feedback. This is a significant gap for a mutation tool.

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

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

The description is a single sentence of 15 words, front-loaded with the core action. Every word adds value, and there is no redundancy or filler. It is an example of efficient writing.

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 three flat parameters and no output schema, the description is minimally adequate. It explains the purpose and the type of feedback. However, it lacks behavioral context (e.g., idempotency, side effects) which would be expected given no annotations. It is not incomplete but leaves gaps.

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

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

Schema description coverage is 100%, so the schema already documents jobId, feedback (with enum), and leadEmail. The description adds no additional meaning beyond 'positive or negative' which is already in the enum. Baseline 3 is appropriate as the schema does the heavy lifting.

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 submits positive or negative feedback on an AI-generated lead message, which is a specific verb+resource. It distinguishes from sibling tools like preview_message or send_to_campaign, which deal with sending or previewing messages, not feedback. However, it could be more precise about the scope of 'feedback' (e.g., per user or per job).

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 provide any guidance on when to use this tool versus alternatives, nor does it mention prerequisites or exclusions. It only states the action, leaving the agent to infer usage context without explicit when-to-use or when-not-to-use instructions.

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 carry the full behavioral burden. It only says 'Sync', which implies a data transfer operation but does not disclose important traits: whether the sync is additive or destructive, how duplicates are handled, authentication requirements, or potential side effects on existing local records. This is insufficient for an operation that modifies data.

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 with a single sentence. It is front-loaded and includes the key action and resources. However, it is borderline under-informative; a slightly longer description adding behavioral details would be more valuable without sacrificing 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 that there is no output schema and no annotations, the description leaves significant gaps. For a sync operation, the agent needs to know the exact nature of the sync (create, update, merge), error handling for missing listId, and any prerequisites. The description fails to provide a complete picture for safe and 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 input schema has 100% coverage for the single parameter 'listId', with a clear description. The tool description does not add any extra meaning or context beyond what the schema already provides, so a baseline score of 3 is appropriate.

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

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

The description uses a specific verb 'Sync' and clearly identifies the resources: 'Hunter.io lead list' into 'local lead records'. This effectively distinguishes the tool from siblings like list_lead_lists or create_lead_list, which serve different purposes.

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

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

The description implies usage when you want to import a Hunter.io lead list into local records, but it provides no explicit guidance on when to use this versus alternatives (e.g., when to sync vs. simply list leads or create a new list). No exclusion criteria or prerequisites are mentioned.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. It correctly identifies the operation as mutating ('enable or disable'), but does not disclose side effects (e.g., whether disabling a channel drops active campaigns, requires re-auth, or affects current messages in flight). The behavior is partially clear but lacks depth for safe autonomous invocation.

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 action ('Enable or disable') and includes the key information (channels and action scope). It could arguably be even more concise by omitting parenthetical examples, but the current length is efficient and clear.

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 2 required params, no output schema, and no annotations, the description is minimal but adequate for straightforward toggling. However, it lacks details about what happens after a channel is disabled (e.g., queued messages, error states, API idempotency), which would be valuable for an agent. The sibling set includes many tools, but this tool's purpose is simple enough that the description suffices for basic 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?

Schema coverage is 100%, so the schema already documents both parameters (channel enum and enabled boolean). The description adds no additional meaning beyond what the schema provides—it repeats the domain of channels but doesn't explain the significance of enabling/disabling (e.g., whether 'push' refers to browser push or mobile push). Baseline 3 is appropriate.

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

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

Description uses specific verb ('Enable or disable') and explicitly names the resource ('outreach channel') with concrete examples of what channels are affected (whatsapp, sms, push). It clearly distinguishes from sibling tools like 'get_channels_status', which would be for reading status, not modifying it.

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 this is used for toggling channel settings, but provides no explicit guidance on when to use versus alternatives like 'get_channels_status' (for reading) or 'update_sales_agent_config' (for broader config). No exclusions or prerequisites are mentioned.

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

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

With no annotations, the description carries full burden for behavioral disclosure. It only states the update action but does not specify whether unspecified fields are left unchanged, overwritten, or require special permissions. No mention of idempotency, error states, or side effects.

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 7-word sentence, highly concise and front-loaded. However, the brevity sacrifices detail needed for a mutation tool, making it slightly too minimal for optimal clarity.

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 4 parameters, no output schema, no annotations, and 24 sibling tools, the description is far from complete. It fails to clarify expected behavior when only dealId is provided, how status enums interact with stage moves, or what the response looks like. Essential context for safe usage is missing.

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

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

Schema description coverage is 100%, so baseline is 3. The description merely repeats the parameter names ('status, value, or notes') without adding new context such as accepted formats, default values, or relationship between fields. It adds marginal value beyond the schema.

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

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

The description clearly states the verb 'Update' and the resource 'deal', and explicitly lists the three fields (status, value, notes). This distinguishes it from 'create_deal' (creation) and 'move_deal_stage' (stage-only change), though the use of 'or' may imply exclusivity rather than combination.

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 like 'move_deal_stage' (which may only handle stage transitions). The description lacks context for prerequisites, valid combinations of fields, or when each sibling is preferred.

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

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

No annotations are provided, so the description carries the full burden for behavioral disclosure. It only says 'Update...' which indicates a write operation, but fails to disclose any side effects (e.g., whether changes take effect immediately, whether it requires specific permissions, or what happens to active processes). The description is too brief to adequately inform the agent about the tool's behavior.

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

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

The description is a single, well-structured sentence (19 words) that front-loads the verb and resource. Every word is necessary, and there is no redundancy or filler. It is appropriately sized for the tool's complexity.

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 6 parameters (all optional, implying partial updates) and no output schema. The description does not clarify that all parameters are optional, nor does it state what the tool returns (e.g., updated config, success status, or error). Given the lack of an output schema, the description should explain the return value, but it does not, making it incomplete for a mutation tool.

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

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

Schema description coverage is 100% (all 6 parameters have descriptions in the schema). The description's list of fields (enable/disable, cal link, etc.) adds no additional meaning beyond the schema. Per the rubric, baseline is 3 for high coverage, and the description does not introduce new semantics.

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

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

The description uses a specific verb ('Update') and resource ('AI sales agent configuration'), and lists the key fields being updated (enable/disable, cal link, offer price, reply limits, confidence threshold). This clearly distinguishes it from sibling tools like 'get_sales_agent_config' (read-only) and 'process_sales_agent' (likely an action).

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 the tool is used when you need to modify the sales agent configuration, but it does not explicitly state when to use it versus alternatives (e.g., get_sales_agent_config for reading, process_sales_agent for triggering actions). No exclusions or prerequisites are mentioned, so guidance is only implied.

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?

The description notes it 'consumes verification credits', which is a behavioral trait beyond what annotations (none provided) offer. However, it does not explain other expected behaviors like result format, error handling, 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?

The description is short (two sentences) and front-loaded with the core purpose. The second sentence about credit consumption 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?

Given no output schema, the description could be more complete by indicating what the tool returns. For a verification tool with batch support, the description covers its action and a key constraint (credit consumption), but lacks return details.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds no additional parameter meaning beyond the schema, as it does not elaborate on usage specifics or constraints for the two 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 verifies email deliverability for one or more addresses. It uses a specific verb-resource pairing ('verify email deliverability') and distinguishes from sibling tools like enrich_lead and search_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 guidance is provided on when to use this tool versus alternatives, such as deciding between the 'email' and 'emails' parameters. There is no mention of prerequisites or context for verification results.

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