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 mentions optional filtering but doesn't disclose behavioral traits such as performance implications, rate limits, authentication needs, or what happens with large datasets. For a read operation with no annotation coverage, this leaves significant gaps in understanding how the tool behaves.

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, efficient sentence that front-loads the core purpose ('Count entities in a kind') and adds necessary detail ('optionally with a filter') without any wasted words. Every part earns its place, making it highly concise and well-structured.

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 a counting tool with filtering, no annotations, and no output schema, the description is incomplete. It doesn't explain the return value (e.g., integer count), error conditions, or how filtering works semantically, leaving the agent with insufficient context to use the tool effectively beyond basic parameter passing.

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 all parameters (kind, field, value) with their descriptions and requirements. The description adds minimal value by hinting at the optional filter but doesn't provide additional semantics beyond what the schema specifies, meeting the baseline for high coverage.

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

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

The description clearly states the verb ('count') and resource ('entities in a kind'), making the purpose specific and understandable. It distinguishes from siblings like datastore_filter (which likely returns entities) and datastore_list_kinds (which lists kinds), though it doesn't explicitly name alternatives. The optional filter mention adds useful context.

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

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

The description provides no guidance on when to use this tool versus alternatives like datastore_query or datastore_filter, nor does it mention prerequisites or exclusions. It implies usage for counting with optional filtering but lacks explicit context for selection among 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?

No annotations are provided, so the description carries the full burden. It mentions the query behavior ('simple equality filter'), but lacks details on permissions, rate limits, error handling, or what happens with no matches. For a query tool with zero annotation coverage, this leaves significant behavioral 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 a single, efficient sentence that directly states the tool's function without unnecessary words. It's appropriately sized and front-loaded, making it easy to understand quickly.

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 (query operation with 4 parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't cover return values, error cases, or usage context, leaving the agent with insufficient information for reliable invocation.

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

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

Schema description coverage is 100%, so the schema already documents all parameters (field, kind, limit, value). The description adds minimal value by implying the parameters are used for filtering, but doesn't provide additional context like examples or constraints beyond what's in the schema.

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

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

The description clearly states the tool's purpose with a specific verb ('Query') and resource ('entities'), and specifies the filtering mechanism ('simple equality filter on any field'). It distinguishes from siblings like datastore_count (counting) and datastore_get (retrieving by key), but doesn't explicitly differentiate from datastore_query, which might offer more complex queries.

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 datastore_query or datastore_list_kinds. It mentions the filtering type ('simple equality filter'), but doesn't specify scenarios where this is preferred over more complex queries or when it's insufficient.

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 gets an entity, implying a read operation, but lacks details on permissions, error handling (e.g., if the key doesn't exist), rate limits, or return format. This is a significant gap 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 a single, efficient sentence with zero waste. It's front-loaded and appropriately sized for the tool's 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?

Given no annotations, no output schema, and 3 parameters, the description is incomplete. It doesn't cover behavioral aspects like error cases or return values, leaving gaps for an agent to use the tool effectively in a datastore context with siblings like datastore_query.

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 all parameters (key, kind, parent) with descriptions. The description adds minimal value beyond implying key-based retrieval, aligning with the schema but not providing additional syntax or format 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 'Get an entity by its key' clearly states the action (get) and resource (entity), specifying the key as the identifier. It distinguishes from siblings like datastore_count (counting) and datastore_query (querying), but doesn't explicitly differentiate from datastore_filter (which might also retrieve 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?

No guidance is provided on when to use this tool versus alternatives like datastore_filter or datastore_query. The description implies it's for direct key-based retrieval, but there's no explicit context, exclusions, or prerequisites 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 states the tool lists kinds, but doesn't describe traits like whether it's read-only (implied but not explicit), performance characteristics, error handling, or output format. For a tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.

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, efficient sentence that front-loads the core purpose without any wasted words. It's appropriately sized for a simple tool with no parameters, making it easy to parse quickly.

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 (0 parameters, no output schema, no annotations), the description is minimally adequate but incomplete. It covers the basic purpose but lacks details on behavioral traits and output format, which are important for an agent to use it correctly. It meets the minimum viable threshold but has clear 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?

The tool has 0 parameters with 100% schema description coverage, so the schema fully documents the lack of inputs. The description adds no parameter information, which is appropriate here. Baseline is 4 for zero parameters, as no compensation is needed for schema gaps.

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 specific action ('List') and resource ('all available entity kinds (tables) in the Datastore'), distinguishing it from siblings like datastore_get (retrieve specific entities) and datastore_query (complex queries). It precisely communicates what the tool does without 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 usage for discovering available kinds/tables, but provides no explicit guidance on when to use this versus alternatives like datastore_query (which might list kinds indirectly) or prerequisites. It lacks clear when/when-not statements or named alternatives, leaving usage context inferred rather than 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 of behavioral disclosure. It mentions 'optional filters' but doesn't explain what types of filters are supported, how queries are executed (e.g., syntax, performance), or what the output looks like (e.g., format, pagination). This leaves significant gaps for a query tool, making it inadequate for safe and effective use.

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, efficient sentence that directly states the tool's function without unnecessary words. It's front-loaded with the core action, making it easy to parse quickly, though it lacks depth due to its brevity.

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 a query tool with no annotations and no output schema, the description is incomplete. It fails to explain key aspects like query syntax, filter options, result format, or error handling, leaving the agent with insufficient context to use the tool effectively. This is a significant gap for a tool with multiple parameters and sibling alternatives.

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 (kind, limit, offset) with descriptions. The description adds no additional meaning beyond implying filtering capabilities, which doesn't compensate for any gaps. This meets the baseline for high schema coverage but doesn't enhance 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 states the action ('Execute a query') and target ('on entities'), which clarifies the basic purpose. However, it's vague about what 'query' entails compared to siblings like datastore_filter or datastore_list_kinds, lacking specific differentiation. It doesn't fully distinguish itself from alternatives, keeping it at a minimal viable level.

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 siblings such as datastore_count, datastore_filter, datastore_get, or datastore_list_kinds. It mentions 'optional filters' but doesn't specify contexts or exclusions, leaving the agent with no usage direction beyond the basic action.

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