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 'parses and executes' operations, handles errors internally by returning error messages as strings, and returns results as a dictionary. However, it lacks details on permissions, rate limits, side effects (e.g., mutations vs. queries), or performance characteristics, which are important for a tool executing arbitrary GraphQL.

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 stated first, followed by parameter and return details in a structured format (Args, Returns, Raises). Every sentence adds value, but the 'Raises' section could be more concise, as it somewhat repeats error handling mentioned earlier.

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 (executing arbitrary GraphQL with potential mutations), no annotations, no output schema, and 2 parameters with 0% schema coverage, the description is moderately complete. It covers basic operation and error handling but lacks details on security implications, mutation effects, or response structure, which are crucial for safe and effective use.

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 explains that 'query' is a 'Raw GraphQL query or mutation string' and 'variables' is an 'Optional dictionary of variables to use in the operation,' clarifying their roles and formats. This compensates well for the schema's lack of descriptions, though it doesn't provide examples or constraints.

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: 'Execute a GraphQL query or mutation with variables.' It specifies the verb ('execute') and resource ('GraphQL query or mutation'), and distinguishes it from siblings like get_query_details (which likely inspects rather than executes). However, it doesn't explicitly differentiate from get_mutation_details, which might be a sibling for mutation-specific details.

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 'GraphQL query or mutation' and providing parameter details, but it doesn't explicitly state when to use this tool versus alternatives like get_query_details or get_mutation_details. No guidance on prerequisites, error handling scenarios, or specific contexts for use 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?

With no annotations provided, the description carries full burden and does well by disclosing behavioral traits: it explains the operation uses a 'special mutation', describes three possible return outcomes (ID string, None, or error message), and mentions exception handling. It doesn't cover rate limits or auth specifics beyond the token reference.

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, method, returns, raises) and avoids redundancy. It could be slightly more concise by merging some details, but every sentence adds meaningful 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?

Given the tool's simplicity (0 parameters, no output schema), the description is quite complete: it explains what the tool does, how it works, possible outcomes, and error handling. For a no-parameter identity tool, this covers essential context without over-explaining.

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?

Since there are 0 parameters and schema coverage is 100%, the baseline is 4. The description appropriately doesn't discuss parameters, focusing instead on the tool's behavior and output, which adds value 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 specific action ('Get the ID') and target resource ('currently authenticated user'), distinguishing it from sibling tools like execute_query or get_mutation_details. It provides a precise verb+resource combination without being tautological.

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 context by mentioning 'currently authenticated user' and 'API token', suggesting it's for identity verification. However, it lacks explicit guidance on when to use this tool versus alternatives or any prerequisites beyond authentication.

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 that the tool retrieves information (implying read-only behavior) and specifies the return format (YAML-formatted string), which adds useful context beyond basic functionality. However, it does not mention potential limitations like rate limits, authentication requirements, or error handling, leaving gaps in behavioral disclosure 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 first sentence stating the core purpose. Additional sentences efficiently elaborate on what is retrieved and provide parameter and return details in a structured format (Args/Returns sections). Every sentence adds value without redundancy, 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 tool's moderate complexity (1 parameter, no output schema, no annotations), the description is largely complete. It covers purpose, usage context, parameter semantics, and return format. However, without annotations or an output schema, it could benefit from more behavioral details (e.g., error cases, authentication needs) to be fully comprehensive, though what is provided is sufficient for basic understanding.

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 fully. It explicitly defines the single parameter 'mutation_names' as a 'comma-separated list of mutation names to get details for', adding crucial semantic meaning not present in the schema (which only provides a title and type). This fully compensates for the lack of schema descriptions, making the parameter purpose and format clear.

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 specific verbs ('Get detailed information', 'Retrieves') and resources ('GraphQL mutations'), distinguishing it from sibling tools like execute_query (which executes queries) and get_query_details (which focuses on queries rather than mutations). It explicitly mentions what information is retrieved: argument requirements, return type information, descriptions, and example usage.

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 clear context for when to use this tool: to get detailed information about specific GraphQL mutations. It implicitly distinguishes it from get_query_details (for queries) and execute_query (for execution), but does not explicitly state when not to use it or mention alternatives for similar needs. The guidance is sufficient but lacks explicit exclusions or named alternatives.

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 describes what the tool retrieves (argument requirements, return types, descriptions, examples) and the return format (YAML-formatted string), which is helpful behavioral context. However, it doesn't mention potential limitations like rate limits, authentication requirements, error conditions, or whether it's read-only (though 'get' implies safe 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 well-structured and front-loaded with the core purpose, followed by specific details, then clearly labeled 'Args' and 'Returns' sections. Every sentence adds value: the first states the purpose, the second elaborates on what's retrieved, and the parameter/return sections provide essential usage information with zero 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?

For a 1-parameter tool with no annotations and no output schema, the description is reasonably complete: it explains the purpose, parameter semantics, and return format. However, it lacks details about behavioral aspects like error handling, authentication needs, or rate limits, which would be helpful given the absence of annotations. The return format description is adequate but could benefit from more specificity about the YAML structure.

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 adds crucial semantic context: 'query_names' is a 'comma-separated list of query names to get details for', which explains the parameter's purpose and format beyond the schema's basic string type. This significantly enhances understanding, though it doesn't specify format details like case sensitivity or valid query name patterns.

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: 'Get detailed information about specific GraphQL queries' with specific details about what information is retrieved (argument requirements, return type information, descriptions, example usage). It distinguishes from sibling 'execute_query' (which executes queries) and 'get_mutation_details' (which focuses on mutations), though it doesn't explicitly contrast with 'get_authenticated_user_id'.

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 context (when you need detailed information about GraphQL queries rather than executing them), but doesn't explicitly state when to use this tool versus alternatives like 'get_mutation_details' for mutations or 'execute_query' for execution. No explicit 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.