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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the tool creates something ('Create a collection'), implying a write operation, but doesn't address permissions, side effects, error handling, or rate limits. The example response format adds some value, but key behavioral traits like whether this is idempotent or what happens on failure are missing.

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 front-loaded with the core purpose, followed by a structured list of parameters and returns. However, it includes verbose examples and repetitive details (e.g., listing all default values in the description when they're already in the schema), making it longer than necessary. Some sentences, like the full example response, could be trimmed for efficiency.

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

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

Given the complexity (11 parameters, no annotations, no output schema), the description is moderately complete. It covers parameter semantics well but lacks behavioral context (e.g., error cases, performance). The example response helps, but without an output schema, more detail on return values would improve completeness. It's adequate but has gaps 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 0%, so the description must compensate fully. It provides detailed semantics for all 11 parameters, including explanations (e.g., 'endpoint: The cluster endpoint URL. Can be obtained by calling describe_cluster'), default values, options for enums like 'metric_type', and usage notes (e.g., for 'db_name'). This adds significant meaning beyond the bare 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 action ('Create a collection') and resource ('in a specified cluster'), with the method 'using Quick Setup' providing additional context. It distinguishes from siblings like 'list_collections' or 'describe_collection' by focusing on creation. However, it doesn't explicitly differentiate from other creation tools like 'create_free_cluster' beyond the resource type.

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 needing to create a collection in a cluster, with a note that 'endpoint' can be obtained from 'describe_cluster', suggesting a prerequisite. However, it lacks explicit guidance on when to use this vs. alternatives (e.g., no mention of when to use 'create_free_cluster' first or how this relates to database operations), and doesn't specify exclusions or edge 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states this creates a free cluster and shows an example return, but lacks critical details: whether this is a mutating operation (implied but not explicit), what 'free' entails (limits, restrictions), if it requires specific permissions, or how long creation takes. The example response hints at asynchronous creation ('cluster is being created...'), but this isn't explicitly explained.

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

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

The description is well-structured with clear sections (Args, Returns, Example) and front-loads the core purpose. The example is helpful but could be more concise by integrating it into the Returns section. Overall, it's efficient with minimal 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 complexity (a creation tool with 2 parameters, no annotations, and no output schema), the description is moderately complete. It covers the basic purpose, parameters, and return example, but lacks usage guidelines, behavioral details (e.g., mutating nature, async behavior), and doesn't fully compensate for the missing structured data. The example return helps but isn't a substitute for a proper output schema.

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

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

The description adds meaningful context for both parameters beyond the schema (which has 0% description coverage). It explains that 'cluster_name' is the name of the cluster to create and 'project_id' is the ID of the project it belongs to, clarifying their roles. However, it doesn't provide format constraints (e.g., length limits for cluster_name) or where to obtain project_id (e.g., from 'list_projects').

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 ('Create a free cluster') and the resource ('in Zilliz Cloud'), making the purpose immediately understandable. However, it doesn't explicitly differentiate this from sibling tools like 'list_clusters' or 'describe_cluster' beyond the creation aspect.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a project_id from 'list_projects'), when not to use it (e.g., if a cluster already exists), or how it differs from other cluster-related tools like 'resume_cluster' or 'suspend_cluster'.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly indicates this is a destructive operation ('Delete'), but doesn't specify whether deletions are permanent/reversible, what permissions are required, rate limits, or error conditions. The example response shows a generic success format but lacks detail on failure modes or side effects. This is inadequate for a mutation tool with zero annotation coverage.

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 moderately structured with clear sections (Args, Returns, Example) but contains some redundancy. The 'Returns' section could be more concise, and the example takes significant space without adding critical information beyond the response format. Some parameter explanations (like 'endpoint') are helpful but could be more efficiently integrated.

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 (7 parameters, destructive operation, no annotations, no output schema), the description is partially complete. It covers parameter meanings well but lacks crucial behavioral context like deletion permanence, authorization requirements, and error handling. The example response provides some output format insight but doesn't substitute for proper behavioral transparency.

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

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

The description provides substantial parameter semantics beyond the schema, which has 0% description coverage. It explains the purpose of 'cluster_id', 'region_id', 'endpoint' (including how to obtain it), 'collection_name', 'filter' (with filtering guidance and empty string behavior), 'db_name' (when to use or leave empty), and 'partition_name' (scope of deletion). This compensates well for the schema's lack of descriptions.

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

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

The description clearly states the tool's purpose: 'Delete entities from a collection by filtering conditions or primary keys.' It specifies the verb ('Delete'), resource ('entities from a collection'), and mechanism ('filtering conditions or primary keys'). However, it doesn't explicitly differentiate this from other destructive operations like 'suspend_cluster' or data modification tools like 'insert_entities'.

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 minimal usage guidance. It mentions that the 'endpoint' parameter 'can be obtained by calling describe_cluster,' which hints at a prerequisite relationship, but doesn't explain when to use this tool versus alternatives like other deletion methods or when deletion is appropriate versus modification. No explicit 'when-not' or alternative tool recommendations 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 provided, the description carries full burden for behavioral disclosure. While 'describe' implies a read-only operation, it doesn't explicitly state this is safe/non-destructive. It provides an example output but doesn't describe error conditions, authentication requirements, rate limits, or what happens with invalid cluster IDs. The example is helpful but incomplete for behavioral understanding.

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 well-structured with clear sections (purpose, Args, Returns, Example). The opening sentence is direct, and the example is appropriately detailed. However, the example takes significant space and could potentially be summarized more concisely while still being informative.

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

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

For a single-parameter read operation with no output schema, the description provides adequate coverage of what the tool does and what it returns. The example output is particularly valuable. However, it lacks context about error handling, authentication, and when this operation might fail - important for a tool that presumably queries infrastructure resources.

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 only 1 parameter and 0% schema description coverage, the description compensates well by clearly documenting the single parameter 'cluster_id' in the Args section and explaining it as 'ID of the cluster whose details are to return'. This adds meaningful context beyond the bare schema, though it doesn't specify format constraints or validation rules.

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 'Describe a cluster in detail' - a specific verb ('describe') and resource ('cluster'). It distinguishes from siblings like 'list_clusters' (which lists multiple) and 'query_cluster_metrics' (which focuses on metrics). However, it doesn't explicitly contrast with 'describe_collection' which describes a different resource type.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when you'd use 'describe_cluster' versus 'list_clusters' (for overview vs details) or 'query_cluster_metrics' (for metrics vs configuration). There's no mention of prerequisites or when this operation is appropriate.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It implies this is a read-only operation (consistent with 'describe'), but doesn't explicitly state safety, permissions, or rate limits. The example output adds some behavioral context by showing the return structure, but lacks details on error handling, latency, 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 appropriately sized but not optimally structured. The purpose statement is clear upfront, but the detailed example occupies most of the text. While helpful, the example could be summarized more concisely. The parameter explanations are well-organized but buried after the initial statement.

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 moderate complexity (5 parameters, no annotations, no output schema), the description provides good coverage. The parameter explanations are thorough, and the example output compensates for the lack of output schema. However, it could better address behavioral aspects like error conditions or performance characteristics.

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

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

The description adds significant value beyond the input schema, which has 0% description coverage. It provides clear explanations for all 5 parameters, including practical guidance for 'endpoint' (how to obtain it) and 'db_name' (when to pass or leave empty). This fully compensates for the schema's lack of parameter documentation.

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: 'Describe the details of a collection.' This is a specific verb ('describe') with a clear resource ('collection'), though it doesn't explicitly differentiate from sibling tools like 'list_collections' or 'describe_cluster'. The purpose is unambiguous but lacks sibling comparison.

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 minimal usage guidance. It mentions that the endpoint 'can be obtained by calling describe_cluster' and notes when to pass or leave db_name empty, but offers no explicit guidance on when to use this tool versus alternatives like 'list_collections' or 'describe_cluster'. There's no context about prerequisites or typical use 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?

With no annotations provided, the description carries full burden. It discloses some behavioral aspects: the limit+offset constraint (<16,384), that endpoint can be obtained from describe_cluster, and that db_name can be empty for free/serverless clusters. However, it doesn't cover important aspects like rate limits, authentication requirements, error conditions, or performance characteristics for a complex 12-parameter search 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 appropriately front-loaded with the core purpose, but becomes verbose with detailed parameter documentation and a full example response. While the parameter explanations are valuable given the 0% schema coverage, the structure could be more streamlined. The example response takes significant space but adds concrete value for understanding output format.

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 high complexity (12 parameters, nested objects), no annotations, and no output schema, the description provides substantial context. It documents all parameters thoroughly, shows example output, and explains key constraints. The main gaps are lack of sibling tool differentiation and insufficient behavioral context (auth, errors, performance), but overall it's quite complete for such a complex 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?

With 0% schema description coverage, the description provides extensive parameter documentation that fully compensates. It explains all 12 parameters with detailed semantics, including optional/required status, examples (e.g., rerank_params {"k": 10}), constraints (limit+offset < 16,384), and relationships between parameters. The search_requests structure is particularly well-documented with all sub-fields explained.

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 performs hybrid search with vector similarity, scalar filtering, and reranking. It specifies the verb 'search' and resource 'entities', but doesn't explicitly differentiate from sibling 'search' tool, which appears to be a simpler alternative. The purpose is well-defined but sibling differentiation could be more explicit.

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 the simpler 'search' sibling or other alternatives like 'query'. It mentions that endpoint 'can be obtained by calling describe_cluster', which is helpful but doesn't constitute comprehensive usage guidelines. No explicit when/when-not recommendations 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 provided, the description carries full burden. It mentions that data keys should match collection schema, which is useful behavioral context. However, it doesn't disclose critical traits like whether this is a write operation (implied but not stated), authentication requirements, error handling, rate limits, or what happens on duplicate keys. For a mutation tool with zero annotation coverage, this leaves significant gaps.

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 appropriately sized and well-structured with clear sections for Args and Returns. Every sentence adds value, though the example in Returns could be more concise. It's front-loaded with the core purpose, then details parameters and response.

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 6-parameter mutation tool with no annotations and no output schema, the description provides good parameter documentation and a response example. However, it lacks information about behavioral traits (permissions, side effects, error cases) and doesn't fully explain the relationship with sibling tools. The response example helps but doesn't replace a proper output schema.

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 0% schema description coverage, the description provides excellent parameter semantics beyond the schema. It explains each parameter's purpose: cluster_id and region_id identify infrastructure, endpoint can be obtained from describe_cluster, collection_name must be existing, data format requirements, and db_name usage conditions. This fully compensates for the schema coverage gap.

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

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

The description clearly states the tool inserts data into a specific collection, providing a specific verb (insert) and resource (collection). It distinguishes from siblings like delete_entities (deletion) and create_collection (creation), though it doesn't explicitly contrast with query/search tools that might also interact with collections.

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 by mentioning prerequisites like existing collections and obtaining endpoint from describe_cluster, but doesn't explicitly state when to use this vs alternatives like create_collection (for new collections) or query (for reading). It provides some context but lacks explicit when/when-not guidance.

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

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

With no annotations provided, the description carries the full burden. It discloses that the tool lists clusters 'scoped to API Key,' which implies authentication context. It also describes pagination behavior and provides a detailed example of the return data structure, including fields like cluster_id, status, and connect_address. However, it doesn't mention rate limits, error conditions, or whether this is a read-only operation (though 'List' implies it).

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 appropriately sized and front-loaded with the core purpose. However, the example return structure is quite detailed (11 fields), which adds length but is valuable for transparency. The structure could be slightly improved by separating the example into a distinct section, but overall it's efficient with minimal waste.

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

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

Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is fairly complete. It covers purpose, parameters with semantics, usage guidance, and a detailed return example. However, it lacks explicit mention of authentication requirements (implied by 'API Key'), error handling, or pagination limits beyond the example. For a list operation with no annotations, this is good 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 description adds significant meaning beyond the input schema, which has 0% description coverage. It explicitly defines both parameters: 'page_size: The number of records to include in each response (default: 10)' and 'current_page: The current page number (default: 1).' It also provides usage context: 'If you want to list all clusters, you can set page_size to 100 and current_page to 1.' This fully compensates for the schema's lack of descriptions.

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

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

The description clearly states the tool's purpose: 'List all clusters scoped to API Key in Zilliz Cloud.' It specifies the verb ('List') and resource ('clusters'), and clarifies the scope ('scoped to API Key'). However, it doesn't explicitly differentiate from sibling tools like 'describe_cluster' or 'query_cluster_metrics', which would require a 5.

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

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

The description provides some usage guidance: 'If you want to list all clusters, you can set page_size to 100 and current_page to 1.' This implies usage for pagination, but it doesn't explicitly state when to use this tool versus alternatives like 'describe_cluster' (for detailed info) or 'list_projects' (for different resources). 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 provided, the description carries the full burden of behavioral disclosure. It describes the return format (JSON string with list of names) and edge case behavior (empty array when no collections found), which is helpful. However, it doesn't mention potential error conditions, authentication requirements, rate limits, or whether this is a read-only operation, leaving significant gaps.

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 well-structured with clear sections (Args, Returns, Example) and front-loads the core purpose. However, some sentences could be more concise (e.g., the endpoint explanation is somewhat wordy), and the example formatting could be cleaner.

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 with no schema descriptions and no output schema, the description does a reasonable job explaining parameters and return values. However, for a tool with no annotations and multiple required parameters, it should provide more behavioral context about error handling, authentication, and operational characteristics to be fully complete.

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

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

With 0% schema description coverage, the description fully compensates by providing detailed semantic information for all 4 parameters. It explains what each parameter represents, provides usage guidance for 'endpoint' and 'db_name', and clarifies which parameters are required versus optional. This adds substantial value beyond the bare schema.

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

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

The description clearly states the tool's purpose: 'List all collection names in the specified database.' This is a specific verb+resource combination (list + collection names). However, it doesn't explicitly differentiate from sibling tools like 'describe_collection' or 'list_databases', which prevents a perfect score.

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 some implied usage context by mentioning the endpoint can be obtained from 'describe_cluster' and noting when db_name can be left empty. However, it doesn't explicitly state when to use this tool versus alternatives like 'list_databases' or 'describe_collection', nor does it provide clear exclusions or prerequisites.

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

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

No annotations are provided, so the description carries the full burden. It describes a read-only operation ('List') and hints at dependencies (endpoint from 'describe_cluster'), but doesn't disclose behavioral traits like rate limits, authentication needs, error conditions, or pagination. It adds some context but leaves significant gaps for a tool with no annotation coverage.

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 appropriately sized and front-loaded with the core purpose. The structured sections (Args, Returns, Example) are helpful, but the example could be more concise. Overall, it's efficient with minimal waste, though the formatting could be tighter.

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 required parameters, no annotations, no output schema), the description is moderately complete. It covers the purpose, parameters, and return value with an example, but lacks details on error handling, permissions, or behavioral constraints. It's adequate but has clear gaps for a tool with no structured safety or output 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 description adds meaningful semantics beyond the input schema, which has 0% description coverage. It explains that 'cluster_id' and 'region_id' are IDs, and clarifies that 'endpoint' is a URL obtainable from 'describe_cluster' using 'connect_address'. This compensates well for the schema's lack of descriptions, though it doesn't detail format constraints or examples for all 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's purpose: 'List all databases in the current cluster.' It specifies the verb ('List') and resource ('databases'), but doesn't explicitly differentiate from sibling tools like 'list_clusters' or 'list_collections' beyond the resource type. The purpose is clear but lacks sibling comparison.

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 by specifying the context ('current cluster') and referencing another tool ('describe_cluster') for obtaining the endpoint parameter. However, it doesn't explicitly state when to use this tool versus alternatives like 'list_clusters' or provide exclusions. The guidance is implied but not comprehensive.

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. While it mentions the tool lists projects 'scoped to API Key,' it doesn't address important behavioral aspects like authentication requirements, rate limits, pagination behavior, error conditions, or whether this is a read-only operation. The example response format is helpful but insufficient for comprehensive behavioral understanding.

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 appropriately sized and front-loaded with the core functionality statement. The structure with separate 'Args' and 'Returns' sections is organized, though the example could be more concise. Most sentences earn their place, with only minor verbosity in the example formatting.

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 parameterless listing tool with no output schema, the description provides basic functionality and return format example. However, without annotations and with multiple sibling listing tools, it lacks sufficient context about authentication, scoping implications, and differentiation from similar tools. The example helps but doesn't fully compensate for missing behavioral 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?

The tool has zero parameters with 100% schema description coverage. The description explicitly states 'Args: None,' which correctly communicates the parameterless nature. This provides clear semantic information beyond what the empty schema alone would 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?

The description clearly states the action ('List all projects') and resource ('projects scoped to API Key in Zilliz Cloud'), providing specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'list_clusters' or 'list_collections' beyond mentioning 'projects' specifically.

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 about when to use this tool versus alternatives. The description doesn't mention prerequisites, context for usage, or relationships to other listing tools in the sibling set. It simply states what the tool does without usage 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 of behavioral disclosure. It adds some context, such as default values for limit and offset, constraints on their sum (<16,384), and an example return structure. However, it lacks details on permissions, rate limits, error handling, or whether it's read-only/destructive, which are critical for a query tool with many parameters.

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 front-loaded with a purpose statement and organized into Args and Returns sections, but it's verbose with repetitive parameter details. Some sentences, like the filter explanation, could be more concise. The structure is clear but not optimally efficient, with room for trimming without losing value.

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 (10 parameters, no annotations, no output schema), the description is moderately complete. It covers parameters well and includes an example return, but lacks behavioral details like error cases, performance implications, or how it differs from sibling tools. This leaves gaps for an agent to fully understand the tool's context and usage.

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

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

The schema description coverage is 0%, so the description must compensate. It provides detailed explanations for all 10 parameters, including purposes, defaults, and constraints (e.g., limit+offset <16,384), adding significant meaning beyond the bare schema. This effectively documents the parameters, though some nuances like filter syntax could be more explicit.

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 'Conduct a filtering on the scalar field with a specified boolean expression,' which vaguely indicates a filtering/query operation but lacks specificity about what resource is being queried (e.g., database collection). It doesn't clearly distinguish from siblings like 'search' or 'hybrid_search,' leaving ambiguity about when to use this tool versus others.

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 like 'search' or 'hybrid_search.' The description mentions obtaining the endpoint from 'describe_cluster,' implying a prerequisite, but doesn't clarify the tool's specific use case or exclusions relative to siblings, leaving the agent without clear usage 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 full burden for behavioral disclosure. It partially succeeds by describing the return format with an example and noting parameter constraints (e.g., 'minimum PT30S' for granularity). However, it lacks important behavioral context such as authentication requirements, rate limits, error conditions, or whether this is a read-only operation (though 'query' implies read).

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 appropriately structured with clear sections (Args, Returns, Example) but is quite lengthy due to the exhaustive metric list. While this detail is valuable for parameter semantics, it makes the description less concise. The core purpose is front-loaded, but the extensive parameter documentation dominates the 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?

For a 6-parameter tool with no annotations and no output schema, the description provides strong coverage. It documents all parameters thoroughly, includes a return example, and explains key constraints. The main gaps are the lack of behavioral context (auth, rate limits) and usage guidance relative to sibling tools, but given the parameter complexity, it's reasonably complete.

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

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

Given 0% schema description coverage, the description compensates exceptionally well by providing comprehensive parameter documentation. It explains each parameter's purpose, format requirements (ISO 8601), optionality, usage rules (e.g., 'use with end'), constraints (minimum granularity), and for 'metric_queries', it provides a complete list of available metric names with descriptions and valid 'stat' values. This adds substantial meaning beyond the bare schema.

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

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

The description clearly states the tool's purpose: 'Query the metrics of a specific cluster.' This is a specific verb ('query') and resource ('metrics of a specific cluster'), though it doesn't explicitly differentiate from sibling tools like 'describe_cluster' or 'list_clusters' which might provide different types of cluster information.

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. While it mentions optional parameters and their usage patterns (e.g., 'use with end'), it doesn't explain when to query metrics versus using other cluster-related tools like 'describe_cluster' or 'list_clusters' for different information needs.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds valuable context: the operation is asynchronous ('takes several minutes'), suggests monitoring via 'DescribeCluster API', and indicates the cluster becomes accessible when 'RUNNING'. However, it lacks details on permissions, rate limits, error conditions, or whether this is idempotent.

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 appropriately sized and front-loaded with the core purpose. The Args/Returns sections are structured but slightly verbose in the example. Every sentence earns its place by clarifying parameters, return values, and behavioral context, though the example could be more concise.

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 annotations and no output schema, the description does a fair job: it explains the parameter, return structure, and key behavioral aspects (asynchronous nature, monitoring). However, for a mutation tool with operational impact, it lacks details on error handling, idempotency, and specific prerequisites, leaving gaps in completeness.

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 0% schema description coverage and only one parameter, the description fully compensates by clearly explaining 'cluster_id: ID of the cluster to resume'. This adds essential meaning beyond the bare schema, specifying what the parameter represents and its role in the operation.

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 ('Resume') and resource ('a dedicated cluster in Zilliz Cloud'), making the purpose immediately understandable. It distinguishes from obvious siblings like 'suspend_cluster' and 'describe_cluster' by specifying the resume action. However, it doesn't explicitly differentiate from all potential alternatives like 'create_free_cluster' in terms of when to use each.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., cluster must be suspended), when-not-to-use scenarios, or direct comparisons with sibling tools like 'suspend_cluster' or 'create_free_cluster'. The agent must infer usage from the name and context 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 carries the full burden of behavioral disclosure. It describes the tool as a search operation (implying read-only, non-destructive) and includes an example return structure, which adds context. However, it lacks details on error handling, rate limits, authentication needs, or performance characteristics that would be important for a complex search tool with 16 parameters.

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 well-structured with clear sections (purpose, Args, Returns, Example) and uses bullet-like formatting for parameters. However, it's quite lengthy due to the 16 parameter explanations, which is necessary given the complexity. Some redundancy exists (e.g., repeating parameter names in descriptions), but overall it's efficient for the information conveyed.

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 high complexity (16 parameters, no annotations, no output schema), the description does an excellent job of explaining parameters and providing a return example. It covers the core functionality thoroughly. Minor gaps include lack of error cases, performance limits, or integration notes with sibling tools, but it's largely complete for a search operation.

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

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

The description provides detailed semantic explanations for all 16 parameters, far beyond the 0% schema description coverage. Each parameter is clearly explained (e.g., 'cluster_id: ID of the cluster,' 'data: A list of vector embeddings...'), including defaults, constraints (e.g., 'sum of this value and offset should be less than 100'), and usage notes (e.g., endpoint can be obtained from describe_cluster). This fully compensates for the lack of schema descriptions.

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

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

The description clearly states the tool's purpose as 'Conduct a vector similarity search with an optional scalar filtering expression,' which is specific about the operation (vector similarity search) and includes the optional filtering capability. However, it doesn't explicitly differentiate from sibling tools like 'hybrid_search' or 'query,' which might offer alternative search methods.

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 'hybrid_search' or 'query.' It mentions that the endpoint 'can be obtained by calling describe_cluster,' which is a prerequisite but not usage guidance. There's no explicit when/when-not or comparison with sibling tools.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions cost implications ('will not incur any computing costs... only billed for storage costs'), which is valuable context beyond basic functionality. However, it lacks details about permissions required, whether suspension is reversible, time to suspend, or error conditions.

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 well-structured with clear sections for Args and Returns, and the core purpose is stated upfront. However, the example in the Returns section is somewhat verbose and could be more concise while still being informative.

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 mutation tool with no annotations and no output schema, the description does an adequate job explaining the basic operation and cost implications. However, it lacks important context about prerequisites, side effects, error handling, and how to verify suspension status, leaving gaps for an AI agent to operate safely.

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 0%, so the description must compensate. It explicitly documents the single parameter 'cluster_id' with a clear explanation ('ID of the cluster to suspend'), adding essential meaning beyond the bare schema. Since there's only one parameter, this is sufficient for good 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 ('suspend') and resource ('dedicated cluster in Zilliz Cloud'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'resume_cluster' or 'describe_cluster' beyond the obvious verb difference.

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 about when to use this tool versus alternatives like 'resume_cluster' or 'describe_cluster', nor are there any prerequisites, conditions, or warnings about when suspension is appropriate. The description assumes the user already knows when suspension is needed.

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