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

With no annotations provided, the description bears full disclosure burden. It only says 'Add', implying mutation, but does not explain side effects, whether the insight is appended or replaced, constraints like max length, or whether changes are reversible.

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

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

Single, front-loaded sentence with no unnecessary words. Every element (verb, object, target) earns its place.

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

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

Given simple schema (one string) and no output schema, the description suffices minimally. However, it lacks context about what 'the memo' is, how to access or verify appended insights, and how this tool relates to the sibling database tools.

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

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

Schema description coverage is 100%, so baseline is 3. The tool description repeats 'business insight' from the schema's property description without adding extra semantic meaning or usage context 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?

Description uses specific verb 'Add' and resource 'business insight' targeting 'the memo', clearly indicating the action. Among sibling tools (read_query, write_query, etc.), none relate to adding insights, so it stands out distinctly.

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

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

No guidance on when to use this tool versus alternatives. While siblings are database-oriented, the description does not explicitly state scenarios, prerequisites, or exclusions.

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

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

No annotations are provided, so the description carries full burden. It only says 'Create a new table' without disclosing behavioral traits: whether it fully executes the query, whether confirmation is shown, effects on existing data, or error handling. The word 'Create' implies mutation but no safety details are given.

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

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

The description is a single short sentence, concise and to the point. However, it could add more value in a sentence or two without being verbose.

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 one parameter and no annotations or output schema, the description is insufficient. It doesn't address return value, error conditions, or how it differs from similar sibling tools. The agent lacks context to decide when to call this tool 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 coverage is 100%, so the parameter 'query' is already documented with description 'CREATE TABLE SQL statement'. The description adds no additional meaning beyond the schema. Baseline 3 is appropriate.

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

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

The description states 'Create a new table in the SQLite database', which is clear but generic. It does not distinguish this tool from siblings like 'write_query' that could also create tables, especially since SQLite allows table creation via arbitrary SQL.

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 no guidance on when to use this tool versus alternatives such as 'write_query' or 'read_query'. It does not mention prerequisites (e.g., database must already exist) or limitations (e.g., no support for temporary tables).

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states 'show structure' but does not specify what 'structure' entails (e.g., column names, types, constraints), whether it requires permissions, or if it's read-only (implied but not explicit). This leaves gaps in understanding the tool's behavior and limitations.

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

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

The description is a single, clear sentence: 'Show structure of a specific table'. It is front-loaded with the core purpose, has no redundant words, and efficiently communicates the essential action without unnecessary elaboration, 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 complexity (a read operation with one parameter) and lack of annotations or output schema, the description is incomplete. It does not explain what 'structure' includes (e.g., schema details), potential errors (e.g., if table doesn't exist), or return format, leaving the agent with insufficient context for reliable 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 input schema has 100% coverage, with 'table_name' fully described as 'Name of the table to describe'. The description adds no additional parameter semantics beyond this, such as format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate, as the schema handles the parameter documentation adequately.

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

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

The description 'Show structure of a specific table' clearly states the verb 'show' and resource 'structure of a specific table', making the purpose evident. However, it does not explicitly differentiate from siblings like 'list_tables' (which likely lists table names) or 'read_query' (which might query table data), leaving room for ambiguity in sibling 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. It does not mention prerequisites (e.g., table must exist), exclusions (e.g., not for querying data), or refer to sibling tools like 'list_tables' for discovery or 'read_query' for data retrieval, offering minimal 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?

No annotations are provided, so the description carries the full burden. 'List all tables' clearly indicates a read-only enumeration, but it does not disclose potential performance implications, whether system tables are included, or any other behavioral nuances. This is adequate but not rich.

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, simple sentence of six words that precisely states the tool's function. It is front-loaded and contains no filler, earning a perfect score for conciseness.

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

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

For a zero-parameter list operation with no output schema and no annotations, the description gives the essential purpose. However, it does not specify the return format (e.g., an array of table names) or whether the list is ordered or filtered. A brief note about the output would improve 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?

The tool has zero parameters, so there is no parameter information to convey. The baseline of 4 applies because the description needs to add nothing beyond the schema, which is empty.

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 'List' and clearly identifies the resource as 'tables in the database'. This distinguishes it from siblings like execute_query and get_table_schema, which operate on query results or individual table schemas.

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

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

The description implies the tool is for enumerating database tables, which provides clear context for when to use it. It does not explicitly exclude alternatives or mention siblings, but the purpose is self-evident in a database toolset.

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 what gets logged (unusual interactions based on probability classes) but doesn't disclose behavioral traits such as whether this is a read or write operation, permission requirements, rate limits, or what happens after logging (e.g., stores data, triggers alerts). The focus is on criteria rather than tool behavior, leaving gaps in transparency.

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

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

The description is structured with bullet points for probability classifications, which is clear but verbose. It's front-loaded with 'Conversation Variation analysis', but the content is overly detailed for criteria rather than the tool's purpose. Some sentences could be condensed, and it includes unnecessary repetition (e.g., listing examples for each class). It's not optimally concise for a tool description.

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 (8 required parameters, no annotations, no output schema), the description is incomplete. It focuses on logging criteria but doesn't explain what the tool does with the input (e.g., queries logs, analyzes data, stores entries). Without annotations or output schema, it should provide more context on behavior and results, but it falls short, leaving the agent unclear on the tool's function.

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 8 parameters thoroughly. The description doesn't add meaning beyond the schema; it mentions probability classifications (HIGH, MEDIUM, LOW) which align with the 'probability_class' parameter's enum, but this is redundant. With high schema coverage, the baseline is 3, as the description doesn't compensate with additional param insights.

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 'Conversation Variation analysis' and 'automatically log unusual or noteworthy interactions', which gives a vague purpose but doesn't specify what the tool actually does (e.g., query logs, analyze conversations, or create logs). It's more about criteria for logging than the tool's function. The title is null, and the name 'log-query' suggests querying logs, but the description focuses on monitoring criteria without clearly stating the tool's action.

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

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

The description provides criteria for when interactions are logged (MEDIUM and LOW probability classes), but it doesn't explicitly state when to use this tool versus alternatives like 'read-logs' or 'write_query'. It implies usage for logging based on probability, but lacks clear guidance on tool selection, prerequisites, or exclusions compared to 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 retrieval but fails to specify if this is a read-only operation, what permissions are needed, or details about rate limits or pagination. This leaves significant gaps in understanding the tool's behavior.

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

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

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and wastes no space, 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 (4 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavioral traits, usage context, and output format, leaving room for improvement 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?

The input schema has 100% description coverage, clearly documenting all four parameters with details like defaults and formats. The description adds no additional meaning beyond the schema, so it meets the baseline score of 3 without compensating for any 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 action ('retrieve') and resource ('logged conversation variations from the database'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'log-query' or 'read_query', which might have overlapping functionality, so it misses the highest 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 no guidance on when to use this tool versus alternatives such as 'log-query' or 'read_query', nor does it mention any prerequisites or exclusions. This lack of context leaves the agent without clear usage instructions.

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

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

With no annotations provided, the description carries the full burden. It discloses that this tool executes SELECT queries (implying read-only behavior), provides a detailed schema reference for the main table, and includes a concrete example showing query structure and limitations (LIMIT 5). However, it doesn't mention potential errors, performance considerations, or authentication requirements.

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 schema reference and example are useful additions, though the example could be more concise. Every sentence earns its place by providing necessary context for query construction.

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 SELECT queries), no annotations, and no output schema, the description does well by providing a detailed table schema and example. However, it lacks information about return format, error handling, or query limitations beyond the example, leaving some gaps for a tool with significant behavioral implications.

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

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

Schema description coverage is 100% (the single parameter 'query' is fully described in the schema as 'SELECT SQL query to execute'), so the baseline is 3. The description adds value by providing a schema reference and example query that clarifies what constitutes a valid query, but doesn't add syntax or format details beyond what the schema implies.

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 ('Execute a SELECT query') and resource ('on the SQLite database'), distinguishing it from sibling tools like write_query (which presumably handles writes) and list_tables/describe_table (which handle metadata). The description explicitly mentions SELECT queries, which helps differentiate from other database operations.

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 through the example (showing a SELECT query on the chat_monitoring table), but doesn't explicitly state when to use this tool versus alternatives like read-logs or log-query (which might be for specific log access). There's no guidance on prerequisites, error conditions, or explicit exclusions.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool executes queries but doesn't mention critical aspects like whether it requires specific permissions, if changes are reversible, potential side effects (e.g., data loss), error handling, or transaction behavior. This is a significant gap for a mutation tool.

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

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

The description is a single, efficient sentence that directly states the tool's function without any unnecessary words. It is front-loaded with the core action and resource, making it easy to parse and 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 complexity of a mutation tool with no annotations and no output schema, the description is incomplete. It lacks information on behavioral traits (e.g., safety, permissions), expected outputs, error conditions, and how it differs from sibling tools. This leaves the agent with insufficient context for reliable tool selection and 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?

The schema description coverage is 100%, with the single parameter 'query' documented as a 'Non-SELECT SQL query to execute'. The description adds value by specifying the allowed query types (INSERT, UPDATE, DELETE), which clarifies the parameter's semantics beyond the schema's generic 'Non-SELECT' label. However, it doesn't provide additional details like syntax 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 with specific verbs (INSERT, UPDATE, DELETE) and resource (query), making it evident this executes data manipulation SQL statements. However, it doesn't explicitly distinguish itself from sibling tools like 'create_table' or 'log-query', which might also involve database operations.

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 'read_query' for SELECT queries or 'create_table' for table creation. It mentions the types of queries (INSERT, UPDATE, DELETE) but doesn't specify contexts, prerequisites, or exclusions, leaving the agent to infer usage from the tool name alone.

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