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

No annotations exist, so the description carries the full burden. It discloses critical behaviors: overwriting existing tables ('WARNING: Overwrites the table if it already exists'), persistence without auto-cleanup, column inference from the first data item, and immediate row insertion. This goes well beyond a minimal description.

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 and contains useful sections (warning, usage, returns). It is slightly redundant—'IMPORTANT' repeats the overwrite warning—and the inaccurate 'Args' section adds confusion. Overall it is well-structured but could be tightened.

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 mutating database tool with no annotations and no output schema, the description covers the essential aspects: what it does, how it infers schema, overwriting behavior, return summary, and distinction from append tool. It lacks details on error handling or atomicity, but is sufficient for most agent use cases.

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 already covers both parameters (100% coverage). However, the description's 'Args' line says 'table_input: Object containing table_name and data', which contradicts the schema's flat structure of top-level table_name and data. This could mislead the agent into passing a nested object, and no additional parameter semantics are provided.

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 opens with a specific verb+resource: 'Creates a new SQLite table from the provided data AND immediately inserts all rows.' This clearly distinguishes it from the sibling insert_data_into_table by stating it is a single-step operation and explicitly directs users to the alternative for appending rows.

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?

Explicit when-to-use guidance is provided: 'Use this as a single step to store fetched API data — no need to call insert_data_into_table afterwards. Use insert_data_into_table only to append more rows to an already-existing table.' This names the alternative tool and specifies the exact use case for each.

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 explicitly discloses the irreversible nature and complete data loss ('This action is irreversible — all data in the table will be lost'), which is critical for a destructive operation. It also mentions the return format. However, it does not detail other side effects like cascading deletes or error conditions, so it is not a perfect 5.

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 concise and front-loads the primary purpose, followed by usage context, a warning, and then parameter/return details. However, the 'Args' section contains a naming mismatch ('table_name_input' vs 'table_name') that detracts from clarity, preventing a perfect score.

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 destructive tool with no output schema, the description adequately covers the key behavioral context (irreversibility, data loss), the return type (success or error), and the use case. It does not discuss edge cases like table non-existence or permission requirements, but given the tool's simplicity, this is sufficient. A score of 4 is appropriate.

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 already covers 100% of the single parameter (table_name) with a clear description. The tool description's 'Args' section introduces 'table_name_input' instead of the schema's 'table_name', which is a minor inconsistency. It adds little semantic value beyond what the schema provides, so a baseline score of 3 applies.

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 'permanently deletes (drops) a table from the SQLite database', using a specific verb and resource. It also explains the use case ('free up space or remove tables that are no longer needed'), which distinguishes it from the many read-oriented sibling tools.

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 on when to use the tool ('free up space or remove tables that are no longer needed'), but it does not explicitly contrast it with alternatives or mention when not to use it. This is a clear context without exclusions, fitting the '4' level.

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 present, so the description carries full burden. It discloses the side effect (SQLite table write), the return format (dict with table_name, columns, rows_inserted, sample), and a specific instruction to cite vectorIds and reference period in the final response. However, it does not state behavior if the table already exists or whether the operation is transactional, which is a modest gap.

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 (preferred usage, workflow, args, returns, important note) and front-loads the core purpose. It is somewhat verbose but every section serves a purpose; there is minimal redundancy (e.g., 'single call' is repeated) which keeps it from a perfect 5.

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?

With 5 parameters, no output schema, and no annotations, the description is remarkably complete: it explains the return value shape, gives a concrete code example, defines the workflow, and includes an important citation instruction. It gives an agent everything needed to invoke the tool correctly and interpret results.

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%, and the schema already explains each parameter (vectorIds, table_name, sample_size, startRefPeriod, endRefPeriod). The description's Args block restates the same information without adding substantive detail beyond what the schema provides, so the baseline of 3 applies.

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 a specific action: 'Fetches data for multiple StatCan vector IDs in a single API call and immediately stores the results in a SQLite table'. It explicitly contrasts with the slower per-series pattern, distinguishing it from sibling tools like get_data_from_cube_pid_coord_and_latest_n_periods and create_table_from_data + insert_data_into_table.

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

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

The description provides explicit when-to-use guidance: 'USE THIS TOOL whenever you need data for multiple provinces, age groups, industries, or any other breakdown.' It also provides a step-by-step workflow showing the tool's place in the pipeline and names the alternative it replaces, giving the agent clear decision criteria.

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 present, so the description carries full weight. It discloses critical behavioral traits: SSL is disabled (a security-relevant warning), results are paginated, and the final response must cite the ProductId and Title. This is exemplary transparency for a listing 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 well-structured and front-loaded: it states the core purpose first, then adds endpoint mapping, behavioral notes, pagination, alternative, and a final citation requirement. Every sentence contributes new information without redundancy. It is compact yet complete.

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?

Despite the lack of annotations and output schema, the description covers all essential operational aspects: what the tool returns, pagination with defaults, the SSL warning, the preferred alternative, and citation requirements. For a list endpoint with only two parameters, this is remarkably 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?

The input schema fully documents both parameters (limit and offset) with defaults and descriptions. The description only reiterates the pagination pattern already captured in the schema, adding no new semantic meaning. Baseline 3 is appropriate because the schema handles parameter semantics completely.

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 provides a complete inventory of data tables with dimension-level details, and it explicitly distinguishes itself from the sibling search_cubes_by_title by positioning itself as the broad-list alternative. The specific verb 'provides' and resource 'data tables' make the purpose unambiguous.

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 clear usage context: it is for browsing the full inventory, with pagination instructions. It explicitly says to prefer search_cubes_by_title when looking for a specific cube, providing an alternative. This exceeds the minimum and fully clarifies when to use the tool.

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

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

With no annotations provided, the description carries the full burden of disclosure. It reveals important behaviors: disables SSL verification, excludes dimension/footnote info, paginates with defaults, and mandates source citation. However, it omits details about response structure or error semantics, which would further enhance 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 well-structured: it opens with the main purpose, then key caveats, then pagination details, followed by a clear alternative, and ends with an important citation requirement. Every sentence earns its place, and the information is front-loaded.

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

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

For a two-parameter listing tool with no output schema, the description is complete. It covers the tool's scope (inventory of tables), exclusions (dimension/footnote), pagination behavior, a relevant alternative, and a required citation practice. No critical gaps remain.

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%, and the description only reinforces the existing parameter documentation (offset/limit for pagination). It adds no new meaning beyond the schema's own descriptions, so the baseline of 3 applies.

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 primary function: 'Provides a complete inventory of data tables available via the API', and differentiates it from siblings by noting it excludes dimension/footnote information ('lighter version'). It also explicitly mentions the corresponding endpoint, reinforcing its specific purpose.

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

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

The description provides explicit usage guidance by stating 'Prefer search_cubes_by_title if you know what you're looking for,' which names an alternative and gives a clear condition for choosing it. It also explains pagination usage with offset/limit, giving actionable 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. It discloses that SSL verification is disabled, describes the response pre-flattened format, lists all raised exceptions (HTTPStatusError, ValueError, Exception), and mandates citing VectorId and Release Time in responses. This is thorough for a read-only data fetch 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 relatively long but every section adds value: warnings about release-date confusion, large responses, SSL, return format, exceptions, and citation requirement. The use of headings and bullet points improves scannability, making the length justified.

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

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

The description is fully self-contained for a complex tool: it explains the core distinction from siblings, the response structure, error conditions, pagination advice, and a mandatory citation rule. Since there is no output schema and no annotations, this description provides all necessary context for an agent to select and invoke the tool correctly.

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 covers 60% of parameters (limit, offset, vectorIds). The description adds crucial semantic clarity for the two date parameters (start/endDataPointReleaseDate) by explaining they are release dates formatted as YYYY-MM-DDTHH:MM, and clarifies how limit/offset relate to pagination. This compensates well for the schema 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 fetches bulk data for multiple vectors filtered by release date range, explicitly distinguishing it from get_sdmx_vector_data which filters by reference period. The verb 'fetches' is specific and the resource/scope is well-defined.

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

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

The description provides explicit guidance on when to use this tool (data released within a date/time window) versus when to use get_sdmx_vector_data (reference period), and even notes that get_sdmx_vector_data is more reliable. It also warns about large responses and recommends narrowing with fewer vectorIds or using offset/limit pagination.

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?

The description discloses important behavioral traits including disabled SSL verification, the corresponding REST endpoint, and a mandatory citation requirement with ProductId and Title. With no annotations provided, this information is valuable for safe and correct 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 well-structured with a clear lead sentence, endpoint mapping, return type, and an important usage note. Every line contributes meaningful information without redundancy.

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

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

For a simple one-parameter GET tool, the description adequately covers the operation, return type, and data attribution requirements. It lacks explicit alternative guidance and error handling details, but is otherwise complete for the tool's complexity.

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 only specifies a date string, while the description adds the required format (YYYY-MM-DD) and clarifies that the date refers to the update date. This compensates for the 0% schema coverage and provides essential context for the single parameter.

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 retrieves a list of data tables/cubes that were updated on a specific date, using a specific verb and resource. The 'changed' qualifier distinguishes it from sibling tools like get_all_cubes_list and get_changed_series_list.

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 date-specific changed cubes but does not explicitly mention alternatives or when not to use this tool. There is no direct comparison with sibling tools such as get_all_cubes_list or get_changed_series_list.

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 discloses several notable behaviors: automatic padding to 10 dimensions, disabling SSL verification, the return type (Dict[str, Any]), and specific exceptions (httpx.HTTPStatusError, ValueError, Exception). It also includes an important citation requirement for the final response. This is strong transparency for a read operation, though it does not mention any potential side effects or permission 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 well-structured and appropriately sized for a tool with error handling and usage notes. It starts with the core purpose, then lists the endpoint, return type, exceptions, and an important usage note. The only redundancy is the coordinate padding mention, which also appears in the schema, but this does not significantly detract from the overall clarity.

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 tool with no output schema and no annotations, the description provides a good amount of context: it explains the purpose, endpoint, return type, error conditions, and a critical citation requirement. It lacks a concrete example of the return value and does not discuss rate limits or pagination, but given the tool's simplicity and the presence of exception documentation, it is 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?

Schema description coverage is 50%, with coordinate having a description but productId having none. The tool description adds semantic meaning by clarifying that productId is a 'Cube ProductId' (a cube identifier) and mentions coordinate padding, which partially compensates for the schema gap. However, it does not explain how to obtain or validate productId, leaving some ambiguity. The coordinate format is already covered in the schema, so the description adds limited extra value.

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 action: 'Retrieves changed series data (data points that have changed) using Cube ProductId and Coordinate string.' This is a specific verb and resource, and the mention of using both ProductId and Coordinate distinguishes it from sibling tools like get_changed_series_data_from_vector and get_changed_series_list.

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 input method (ProductId and Coordinate) and references the corresponding POST endpoint. However, it does not explicitly state when to use this tool over alternatives such as get_changed_series_data_from_vector, nor does it provide exclusions or alternative suggestions. The guidance is mostly implicit from the tool name and description.

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

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

With no annotations, the description carries the full burden and discloses key behaviors: it 'Disables SSL Verification,' notes the HTTP endpoint, specifies return type, and lists possible exceptions. It also includes an important instruction about citing the VectorId, which is useful context beyond basic functionality.

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 sections for purpose, endpoint, returns, raises, and an important note. It is slightly verbose but each sentence serves a purpose, though the phrase 'data points that have changed' is redundant with the tool name.

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 tool with one parameter, no annotations, and no output schema, the description covers purpose, security behavior, endpoint, return type, exceptions, and a usage instruction. It lacks parameter semantics and detailed return structure, but overall provides a solid context for 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 0%, yet the description does not explain the 'vectorId' parameter beyond its name. It repeats 'Vector ID' without providing details on format, range, or how to obtain it, adding no semantic value over 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 'Get changed series data (data points that have changed) for a series identified by Vector ID,' specifying a distinct verb, resource, and identifier. It differentiates from siblings like get_changed_series_data_from_cube_pid_coord, which uses a different identification method.

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, nor mentions any sibling tools or exclusions. It only describes the operation itself, leaving the agent to infer applicability from the name.

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, so description fully carries the burden. Discloses SSL verification disabled, lists exception types, and mandates citation of VectorId. This goes beyond basic read-op disclosure.

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?

Starts with the main purpose, then adds relevant details. A bit verbose with the exception list, but all sentences contribute to understanding. Well-organized.

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 one-param tool with no output schema, description covers return type, fields, and exceptions. Could include an example, but overall complete enough.

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 only has a bare string 'date' with 0% coverage. Description adds the required format (YYYY-MM-DD) and endpoint path, making the parameter's meaning 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?

Clearly states it gets the list of series updated on a date, with specific fields (vectorId, productId, coordinate, releaseTime) and the endpoint. Distinguishes from siblings like get_changed_cube_list by focusing on series.

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?

Provides clear context: use to get a list of changed series for a specific date. No explicit alternatives or exclusions, but the scope is well-defined and the endpoint is given.

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 discloses return type and possible exceptions (httpx.HTTPStatusError, ValueError) and a citation requirement, but does not explicitly state read-only behavior or any auth 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 moderately sized with a clear first sentence and structured sections for returns/raises/notes. It is not overly verbose but includes standard exception boilerplate.

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 lack of annotations and output schema, the description provides return type and error types, but does not detail the dictionary structure or list available code sets, leaving some gaps for a complete 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?

Input schema has 0 parameters, so baseline is 4. The description adds no parameter-specific information, which 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 clearly states 'Retrieves definitions for various code sets used by the API' with examples, making the purpose unambiguous and distinguishing it from sibling tools that deal with cubes, tables, or SDMX.

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 explicit when-to-use guidance or alternative tools, though the purpose implies usage for code set definitions. No exclusions or alternatives 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. It discloses a notable behavior: 'Disables SSL Verification.' It also explains the summary mode's behavior (strips French translations, shows 3 sample members, adds _next_steps) and explicitly warns about safe context window sizes. It lists exceptions and even adds a mandatory citation instruction. While it could explicitly state read-only intent, the retriever verb and non-mutating nature are clear enough.

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 reasonably sized for the tool's complexity and front-loaded with the primary purpose. Each section (endpoint mapping, summary guidance, alternatives, return/raises, citation note) adds value. It is slightly verbose with repetitive emphasis (e.g., summary=True repeated), but overall well-organized and efficient.

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 output schema and no annotations, the description compensates well. It describes the return type (Dict[str, Any]), explains the summary vs. full modes, lists exceptions, and provides situational guidance (e.g., safe for all context window sizes). The mention of _next_steps guidance and citation requirements gives operational completeness. Nothing essential is missing for an agent to invoke and use the tool correctly.

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

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

Schema description coverage is only 50% (summary is described, productId is not). The description goes beyond the schema by thoroughly explaining the summary parameter: 'strips noise (French translations, archive codes, footnotes) and shows only 3 sample members per dimension with _next_steps guidance.' The productId parameter is only mentioned as 'using its ProductId', but the endpoint mapping and citation note ('including the ProductId (pid) and the Title') give additional context. Given the strong added meaning for summary, this is above baseline.

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 opens with a clear statement of purpose: 'Retrieves detailed metadata for a specific data table/cube using its ProductId.' It lists specific content (dimension info, titles, date ranges, codes) and maps to a concrete endpoint (POST /getCubeMetadata). Sibling differentiation is also explicit via references to get_sdmx_structure and get_series_info, making it easy to distinguish from related tools.

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

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

The description provides explicit when-to-use guidance: 'Start with summary=True (default)... Set summary=False only if you need the full raw member list or all API fields.' It also names alternatives for adjacent tasks: 'To browse dimension codes... use get_sdmx_structure. To resolve a coordinate to a vectorId, use get_series_info.' This covers both usage context and 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, the description carries full burden and excels. It discloses server-side filtering, sparse-sample wildcard behavior, the OR-syntax bug and auto-correction, the 406 error when combining lastNObservations with start/endPeriod, the 500-row cap, and the required source citation. This is exemplary behavioral disclosure.

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 long but exceptionally well-structured with clear sections, examples, warnings, and limitations. Every sentence conveys actionable information; there is no filler. The front-loaded purpose and bolded IMPORTANT notes make it easy to scan and use.

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, 5 parameters, no annotations, and no output schema, this description is remarkably complete. It covers input construction, time filtering, output fields, row caps, error behavior, helper-tool usage, and even user-facing citation obligations. It leaves no critical operational gap.

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 80% and the schema already documents key syntax and time parameters. The description adds valuable semantics on top: wildcard sparse sampling, member-ID versus codelist positions, lastNObservations=12 means one year of monthly data, and the rejection when combining time filters. However, productId remains undocumented in both schema and description, which prevents a perfect score.

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 opens with a specific verb and resource: 'Fetch filtered time-series observations from a StatCan table via SDMX REST.' It immediately clarifies the server-side filtering behavior and distinguishes itself from structural/helper tools by requiring get_sdmx_structure first. This clearly positions the tool among siblings.

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

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

The description provides explicit when-to-use and how-to-use guidance: call get_sdmx_structure first, use member IDs instead of SDMX codelist positions, and use get_sdmx_key_for_dimension for large dimensions. It also warns against wildcard use for dimensions with >30 codes and explains time-filter combinations and restrictions.

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 explains what 'leaf' means and shows the return structure via example, including the 'or_key' field and its role in the get_sdmx_data key. It does not mention edge cases or error behavior, but core behavior is transparent.

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: purpose, use case, definition, example, and integration. Every sentence earns its place, including the example which clarifies expected input/output. Front-loaded with the main action, making it easy to scan.

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?

Despite no output schema, the example provides a complete picture of the return value. The description covers why to use it, when to use it, how it works, and how to apply the result in get_sdmx_data. It is self-contained for the target task.

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 only 50% (only dimension_position has description). The description compensates by showing productId in the example and explaining dimension_position via the 'Paste or_key at position 6' note. However, productId itself lacks a direct description beyond the example.

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 opens with a specific verb ('Return') and resource ('leaf member IDs for a single dimension as a ready-to-use OR key string'). It clearly distinguishes itself from siblings like get_sdmx_data (which consumes the key) and get_cube_metadata (which it explicitly avoids).

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?

States exactly when to use it: 'before get_sdmx_data when a dimension has many codes.' It also explains the benefit of avoiding get_cube_metadata, providing an explicit alternative. Includes a concrete example showing how to integrate with get_sdmx_data.

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

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

With no annotations, the description takes on full behavioral disclosure. It reveals that rows are capped at MAX_SDMX_ROWS, that output is always inline, and that the final response must cite specific fields. It does not mention permissions or error handling, but for a read-only fetch operation these are less critical. The cap and citation requirements are valuable context beyond what annotations would provide.

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 long but well-organized with bullet points and clear sections. Each sentence adds value: purpose, use cases, syntax reference, row cap, and citation requirement. It could be trimmed slightly but remains efficient for a tool with multiple important constraints.

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

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

The tool has no output schema, but the description explains the inline return format, row cap, and citation requirement. It also gives usage context and alternatives. It does not mention error handling or authentication, but for a straightforward fetch operation this is largely sufficient. The complexity is moderate, and the description covers the essential operational details.

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 80%, so most parameters (key, startPeriod, endPeriod, lastNObservations) already have detailed descriptions in the schema. The description adds a cross-reference to get_sdmx_data for key syntax and wildcard warnings, which is helpful, but it does not explain each parameter individually. Baseline 3 is appropriate given the schema's strong 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 starts with a specific verb+resource: 'Fetch SDMX observations and always return rows inline' and immediately states the intended use case ('use this when you need to embed data in an artifact or widget'). This clearly distinguishes it from siblings like get_sdmx_data or get_sdmx_vector_data by emphasizing the inline return behavior.

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?

It explicitly tells when to use the tool with concrete examples (building a chart/table/widget, sorting/filtering a small result set). It also points to an alternative for large dimensions (get_sdmx_key_for_dimension) and references get_sdmx_data for syntax rules, providing clear when-to-use and when-not-to-use 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, the description carries the full behavioral burden and discharges it well: it discloses truncation behavior (codes truncated to 10, _code_count and _truncated flags), key syntax including wildcard and OR operators, and the mandatory requirement to cite _sdmx_url in the final response. This goes far beyond a generic 'fetch data' statement.

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 longer than average but well-structured with a lead sentence, a bullet list for dimension entries, and key construction rules. Each section adds needed domain knowledge (truncation, key syntax, citation). It earns its length, though a few details (like the truncated-code note) could arguably be trimmed.

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 absence of an output schema and annotations, the description covers the essential return shape, truncation, key syntax, and source-citation requirement. The only notable gap is the unexplained productId parameter, which prevents full completeness for an agent trying to invoke the tool blindly.

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 only exposes productId (integer) with no description and coverage is 0%. The description refers to 'a StatCan table' but never explicitly maps productId to the table identifier or explains how to discover valid values. The schema and description leave the single parameter under-documented.

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 opens with a specific verb-resource pairing: 'Fetch the Data Structure Definition (DSD) for a StatCan table via SDMX REST.' It clearly distinguishes this from sibling tools by stating it should be used before get_sdmx_data and by describing the DSD-specific output (dimension codelists, code IDs, parent hierarchy).

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?

Explicitly states 'Use this BEFORE get_sdmx_data to understand the key syntax for that table,' giving clear contextual placement. It also includes key construction rules that explain how the output is consumed downstream. It does not mention conditions where one would not use it or alternative tools, so slightly below a 5.

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 transparency burden. It discloses a specific limitation ('StatCan rejects combining lastNObservations with startPeriod/endPeriod returns 406'), describes the output rows, and includes a mandatory citation requirement. It does not discuss rate limits or auth, but for a read-only fetch operation, the disclosed information is substantial.

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 somewhat long but well-structured with clear sections for purpose, alternatives, time filtering, limitation, output, and citation. Every section adds necessary information, though it could be tightened without losing content. Front-loading the core purpose and alternative helps quick scanning.

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, lack of output schema, and multiple sibling tools, the description covers all essential aspects: what it does, when to use it, how to find parameters, parameter constraints, output structure, and a critical legal/citation requirement. It is sufficiently complete for an agent to invoke it correctly.

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 75%, and the description adds meaningful context beyond the schema: it gives concrete examples for startPeriod and endPeriod formats (monthly vs annual), demonstrates lastNObservations usage, and warns against combining mutually exclusive filters. The schema already describes most fields, so the description complements rather than repeats.

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 function: 'Fetch time-series observations for a single StatCan vector via SDMX REST.' It also distinguishes this tool from its sibling by calling it a 'Simpler alternative to get_sdmx_data when you already know the vectorId,' which directly addresses the key differentiation.

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?

Explicit usage guidance is given: it names alternative siblings for finding vectorIds ('Use get_series_info_from_cube_pid_coord or get_cube_metadata to find vectorIds') and clarifies when this tool is appropriate versus get_sdmx_data. It also provides concrete examples for parameter usage, making the decision process clear.

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

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

With no annotations, the description carries the full burden, and it delivers: it discloses batch behavior (one or many), automatic coordinate padding, pagination via _guidance, numeric code decoding requirements, and specific exception types. This goes beyond basic expectations and gives the agent a reliable mental model of 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 well-organized with sections for purpose, usage, notes, returns, raises, and an important reminder. It is slightly verbose for an agent-facing description, but every section adds meaningful value and no sentences are purely filler, so it earns a 4 rather than a 5.

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?

Despite having no output schema, the description fully explains what the tool returns (list of series metadata dicts, paginated with _guidance) and explicitly lists all error types with conditions. This makes the tool self-contained for an agent to invoke and interpret results correctly, especially with the important note about citing ProductId and Coordinate.

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%, with item and coordinate descriptions already stating the batch and padding behavior. The description repeats 'Coordinates are automatically padded' and 'Pass one item or many', but adds no new parameter-level semantics beyond what the schema already provides, so the baseline score of 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 uses a specific verb ('Resolve') and clearly defines the resource as '{productId, coordinate} pairs' mapped to 'series metadata' including vectorId, titles, frequency, and UOM. It explicitly states the primary use case ('find vectorIds before fetching data'), effectively distinguishing it from sibling tools like get_series_info_from_vector.

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?

It provides clear context: 'Use this to find vectorIds before fetching data with get_sdmx_data or get_sdmx_vector_data.' This tells the agent when to use the tool, but it does not explicitly mention when not to use it or suggest alternative tools for exclusion scenarios, so it falls short of a full 5.

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 present, so the description carries the full transparency burden. It uniquely discloses 'Disables SSL Verification', documents expected return type, and lists error scenarios. It does not address auth or rate limits, but the safety profile is reasonably conveyed.

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-organized with a lead sentence, endpoint, return, exceptions, and a prominent citation note. No filler; each section adds operational 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?

For a parameter-light tool with no output schema, the description is thorough: it explains what metadata is returned, the output type, exceptions, transport behavior (SSL), and a mandatory citation requirement. This is sufficient for successful 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 provides only an integer 'vectorId' with no description (0% coverage). The description adds that this ID selects the series and must be cited in the final response, giving it a functional role. It does not explain how to obtain or validate a vectorId.

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—requesting series metadata—and identifies the resource/identifier (Vector ID). It also specifies the endpoint, making it distinct from sibling get_series_info even without an explicit 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 the tool is used when you have a Vector ID to fetch metadata, but it does not explicitly state when to prefer it over siblings like get_series_info, nor does it list exclusions/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, the description must carry the burden of behavioral disclosure. It does mention that the return is a 'Dict[str, Any]' describing the schema 'or an error message', giving some insight into error handling. However, it does not specify whether the table must exist, what happens on missing tables, or any side effects (though the operation is read-only). 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 concise and front-loaded with the purpose. However, the Args section is redundant and inconsistent with the schema, and the Returns section could be more compact. It is brief overall but contains a small structural flaw that prevents a perfect score.

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

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

For a simple one-parameter tool, the description provides the return type and error possibility. Yet it omits any detail about the shape of the schema dictionary (e.g., whether it includes column names, data types, constraints) and does not clarify behavior for non-existent tables. Given the lack of an output schema, this is a moderate gap.

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?

Although the schema covers the parameter fully (table_name as a string), the description's Args section is misleading: it says 'table_name_input: Object containing the table_name', which contradicts the schema that expects a direct string. This introduces ambiguity and fails to add meaningful semantic value beyond 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 uses a specific verb ('Retrieves') and clearly states the resource ('the schema (column names and types) for a specific table'). This unambiguously differentiates it from sibling tools like list_tables (which lists tables) and query_database (which fetches data).

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?

There is no explicit or implicit guidance on when to use this tool versus alternatives. No mention of 'use this when you need a table structure' or references to sibling tools like list_tables or query_database. The only context is the tool's purpose, which does not qualify as usage 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, the description carries the full burden. It discloses that the table must already exist, that the operation appends rows, and that it returns a success/failure dict. It also adds an important caveat about citing the data source. However, it doesn't cover error scenarios such as non-existent tables or schema mismatches, keeping it slightly below a perfect score.

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 primary purpose. It includes separate sections for usage, arguments, returns, and an important note. It is reasonably concise, though the inaccurate parameter explanation slightly reduces clarity, keeping it from a perfect score.

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

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

The description covers purpose, usage, return type, and an agent instruction about citing sources. However, the inaccurate parameter description and lack of error handling details leave gaps. Given the tool's simplicity, the description is adequate but could be more accurate and thorough, so a score of 3 is appropriate.

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?

Although the schema has 100% coverage for both parameters, the description's Args section inaccurately describes a single 'table_input' object containing table_name and data, while the actual schema defines flat top-level properties. This misleading parameter explanation actively harms understanding rather than adding value, warranting a score below the 3 baseline.

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 'Appends rows (list of dicts) into an ALREADY EXISTING SQLite table.' It uses a specific verb and resource, and explicitly distinguishes itself from sibling tools like create_table_from_data and fetch_vectors_to_database by emphasizing it is only for adding data to an existing table.

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?

Provides explicit when-to-use guidance: useful for merging data from multiple API calls or appending new time periods. It also explicitly says when NOT to use it, directing users to alternative tools that both create and insert in one call, making the usage context unmistakable.

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

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

With no annotations, the description carries full burden; it adds key context that the database is persistent and that only user-created tables are returned, with an error message on failure. It implies a read-only operation by the verb 'Lists' but does not explicitly state non-destructive 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?

Three short sentences plus a return type declaration; every sentence earns its place. The important note about persistence and cleaning is relevant rather than filler.

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, no-output-schema tool, the description covers the result shape, the scope (user-created tables), and an important operational caution. No critical information is missing.

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 no parameters, so the description needs no parameter details. The baseline of 4 applies, and the description adds sufficient contextual information for a parameterless tool.

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 the specific verb 'Lists' and identifies the resource as 'user-created tables in the SQLite database,' clearly distinguishing it from schema/drop/query siblings. It also states the return type, making the tool's core function unambiguous.

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?

It explicitly says to use this for checking old tables that might need cleaning, providing a concrete use case. However, it does not mention alternatives or when not to use it, so it lacks explicit exclusion 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?

No annotations are provided, so the description carries the full burden. It discloses security risk, potential truncation of results, the return shape, and the requirement to cite data source. However, it does not elaborate on how the tool enforces read-only or handles errors beyond mentioning an error message.

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 a warning, usage preference, args, returns, and citation instruction. It is somewhat verbose but each section serves a purpose. The inaccurate Args line detracts from 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?

Given no output schema, the description provides essential context: security warning, truncation, return format, and citation requirement. It is sufficient for selecting and invoking the tool, though minor parameter confusion and lack of error-handling details leave some 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 schema fully covers the single parameter with 'The SQL query to execute.' However, the description's 'Args: query_input: Object containing the sql_query string' suggests a nested object structure that contradicts the actual schema, which expects a top-level 'sql_query' string. This mismatch could mislead the agent on how to invoke the tool.

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 it 'Executes a read-only SQL query (SELECT or PRAGMA) against the database and returns the results' — a specific verb and resource with explicit scope. It also distinguishes itself from siblings by naming alternatives like list_tables and get_table_schema.

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?

It explicitly advises to avoid untrusted input and queries that modify data, and recommends using more specific tools when possible. This gives clear when-to-use and when-not-to-use 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?

No annotations are provided, so the description carries the burden of behavioral disclosure. It reveals important traits: case-insensitive matching, lite format exclusion, AND logic, result capping, error types, and a mandatory citation requirement. This is substantial coverage beyond the basic search functionality, though it doesn't cover authentication or rate limits.

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 distinct paragraphs for purpose, behavior, citation requirement, and errors. Each sentence contributes unique information without fluff or repetition. It is appropriately sized for the complexity.

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 output schema, the description explains the return format (lite format, excluding dimensions/footnotes) and error conditions. It doesn't enumerate the lite format fields, but that is a known concept from sibling tools. The description is complete enough for an agent to invoke the tool effectively.

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% with both parameters described, so baseline is 3. The description adds extra semantics for search_term (English/French title, case-insensitive) and reiterates AND logic, which adds value beyond the schema. It also clarifies the default behavior of max_results.

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 'searches' and the resource 'data cubes/tables', specifying that it searches English or French titles case-insensitively. This distinguishes it from sibling tools like get_all_cubes_list_lite, which return full lists without search capability. The scope is unambiguous.

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: when searching for cubes by title. It explains AND logic and the max_results cap, which informs usage. Though it doesn't explicitly name alternatives, the context is sufficient to guide 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?

With no annotations, the description carries the full burden. It discloses that the tool writes to SQLite tables, does not return full data, returns a compact summary, and that tables are shared across pids with rows added without affecting other pids. It also notes the citation requirement. This is thorough and transparent.

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 longer than a simple one-liner, but it is well-structured with a clear opening sentence, a 'Use this when' section, a numbered workflow, and an important note. No sentence is wasted; the length is proportional to the tool's complexity.

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?

Despite having only one parameter and no output schema, the description is extremely complete: it explains the storage side effects, the return summary, how to follow up with SQL queries, and the required citation. The integrated workflow makes it a self-contained guide.

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% for productId, so the baseline is 3. The description adds extra context by showing an example invocation (productId=1234567) and explaining how productId maps to the pid column in the stored tables, which is useful beyond the schema's basic type description.

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 explicitly states the tool 'Fetches FULL metadata for a cube and stores it into two normalized SQLite tables...' and distinguishes it from get_cube_metadata which only shows 5 members per dimension. This clearly identifies the tool's unique role.

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 says 'Use this when you need to browse all dimension members or look up vectorIds' and explicitly contrasts with get_cube_metadata, while also providing a numbered workflow involving query_database and fetch_vectors_to_database, giving clear when-to-use and alternative guidance.

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