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

With no annotations, the description must carry the full behavioral burden. It discloses the return value (created filter with ID), but omits potential errors, idempotency, or permission requirements. This is minimal but not entirely absent.

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 with no wasted words. It front-loads the action and then adds context and return behavior.

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 detailed schema covers the complex conditions object and type enum, while the description adds the sales_queue.get use case and return value. It lacks error handling info, but is otherwise sufficient for a create tool.

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

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

Schema description coverage is 100%, so the schema fully documents all three parameters. The description adds no extra parameter meaning beyond what the schema already provides.

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

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

The description clearly states the tool creates a new Pipedrive saved filter with a specific verb and resource. It also distinguishes from siblings (list, delete) by noting its use with miinta.sales_queue.get.

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 gives a clear usage context: creating custom filters for later use with the sales queue. It doesn't explicitly exclude alternatives, but the create/list/delete relationship among siblings is implicit.

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

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

With no annotations, the description carries the full burden of behavioral disclosure. It states the action ('delete') but does not mention irreversibility, permissions, side effects, or return values, which are important for a delete operation.

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

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

The description is two sentences long, front-loaded with the core action, and contains no extraneous information. It uses the available space efficiently.

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 single-parameter delete operation, the description provides the essential context: what to delete, how to specify it, and how to find valid IDs. It stops short of noting destructive implications, but the tool's simplicity reduces the need for more.

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 parameter is already well-documented. The description adds context by telling the user to use miinta.filters.list to find filter IDs, which is helpful but does not introduce new semantic detail about the 'id' parameter itself.

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 specific verb ('Delete') and resource ('Pipedrive saved filter') with a clear scope (by ID). It also distinguishes itself from sibling tools like miinta.filters.list and miinta.filters.create.

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 clearly implies when to use this tool (when you have a filter ID to delete) and provides a direct pointer to miinta.filters.list for finding IDs. It lacks explicit exclusion of alternative tools, but the distinction from siblings is clear.

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

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

With no annotations provided, the description carries the burden of behavioral disclosure. 'List all available' implies a read-only operation, but the description does not explicitly state safety (no side effects) or mention potential limitations like pagination or rate limits. For a straightforward list tool, the basic behavior is implied, but the description adds minimal context beyond what the tool's name suggests.

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-loaded with the main action ('List all available Pipedrive saved filters') immediately followed by practical use context. There is no redundant information, and every clause adds value. It is appropriately sized 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?

Given the tool's simplicity (one optional parameter, no output schema), the description covers the essential purpose, the use case, and the optional parameter. It lacks details about the exact return structure (e.g., fields beyond ID and name), but since the description explicitly mentions discovering IDs and names, it is reasonably complete. The context of sibling tools is clear, and the description enables effective tool selection without needing additional information.

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

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

The input schema has 100% coverage for the single optional 'type' parameter, which is fully described in the schema itself ('Optional filter type to narrow results.'). The description adds a small amount of value by repeating this option ('Optionally filter by type') but does not provide additional semantic detail such as how filtering affects results or examples. The baseline of 3 applies since schema coverage is high.

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

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

The description clearly states the tool's purpose with a specific verb and resource: 'List all available Pipedrive saved filters.' It distinguishes itself from sibling tools like miinta.filters.delete and miinta.filters.create by focusing on listing rather than mutation. The mention of discovering filter IDs and names clarifies the output's utility.

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 explicit context for when to use this tool: 'Useful for discovering filter IDs and names before calling miinta.sales_queue.get.' This gives a clear workflow guidance. It does not explicitly state when not to use it versus create/delete, but for a simple list tool, the usage context is sufficient and distinguishes from the sales_queue tool.

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 alone must convey behavioral traits. It states the purpose (return a payload) and mentions enrichment, implying a read-only operation. However, it doesn't explicitly confirm no side effects, nor does it disclose error behavior, rate limits, or the impact of invalid filters. The mention of filter discovery hints at dependencies, but significant gaps remain.

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 exactly two sentences: the first states what the tool does with specific output details, the second gives a practical usage pointer. Every word adds value, no repetition, and the structure is efficient and front-loaded.

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

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

The tool has a complex schema with nested objects and 5 parameters, yet there is no output schema. The description names the enriched fields but doesn't explain the queue structure, how filters map to sections, or the behavior of options like limits and timezone. It provides a high-level overview but lacks sufficient detail for an agent to confidently invoke it without further exploration.

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%, so the description needed to compensate by explaining parameters. It correctly mentions filters by ID or name, but this is already in the schema. It does not explain 'limits', 'timezone', 'include_people_orgs', or 'now', which are critical for using the tool correctly. The description adds minimal value beyond the schema for most 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 uses a specific verb 'Return' and a clear resource: a structured 'morning queue' payload. It details the mechanism (three Pipedrive filters) and enrichment fields, distinguishing it from sibling tools that manage filters (list, delete, create). This leaves no ambiguity about the tool's 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 explicitly tells users to use miinta.filters.list to discover available filter names, which is a clear prerequisite and points to the correct sibling tool for setup. It implies that filters must exist, but doesn't explicitly state when not to use this tool or mention alternatives for other queue-related needs. This is helpful but not exhaustive.

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