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

With no annotations, the description carries the full burden. It discloses the output format (Markdown) and the transformation nature, but does not mention edge cases, side effects, or how it handles malformed input beyond 'wrong number of cells.' It gives enough to be safe but not exhaustive, so a 3 is appropriate.

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 front-loaded purpose, explicit usage triggers, and a clean Args block. It is efficient but includes a few redundant clarifications (e.g., 'Defaults to reading it as Markdown' appears twice), which slightly reduces 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 3-parameter tool with an output schema present, the description covers purpose, usage, parameters, and output format. It omits only minor details like error behavior or whether the original table is preserved, which are not critical for a straightforward transformation tool.

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

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

Schema coverage is 0%, so the description must compensate, and it does thoroughly. The Args section explains each parameter: text's default reading as Markdown, from_format's accepted values, and aligns' per-column syntax with the meaning of empty. This fully compensates 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 opens with a clear, specific verb and resource: 'Re-align a messy Markdown table so every column lines up.' It explicitly differentiates from siblings by calling itself a focused shortcut for table_convert(..., to_format='markdown'), making its scope unmistakable.

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 conditions for use ('when a Markdown table is ragged, when pipes are not aligned...') and names the alternative (table_convert) with a clear distinction (always outputs Markdown). This leaves no ambiguity about when to select this 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, the description carries the full burden and does so well. It discloses strong behavioral guarantees: 'Rows are never silently dropped, and ragged rows are padded rather than truncated.' It also details the Markdown alignment behavior with East-Asian wide characters counted as two cells. These go far beyond the bare conversion verb and give an 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 front-loaded with a one-sentence summary, immediately followed by usage context and behavioral guarantees, then a structured Args list. Every sentence earns its place: the wide-character note, row-preservation guarantee, and alias examples are all operationally relevant. No filler or repetition.

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 has 5 parameters, no annotations, and no schema descriptions, the description covers all essential information: full parameter semantics, behavioral edge cases, and explicit usage triggers. The output schema exists, so return values do not need to be explained. An agent has enough to select and call this tool correctly without additional investigation.

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

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

Schema description coverage is 0%, so the description must compensate. The Args section fully compensates by explaining each parameter's meaning, acceptable values, aliases (md, htm), example formats ("left,right,center"), and defaults (indent 2, aligns empty). This adds substantial value beyond the schema titles and enables correct invocation.

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+scope: 'Convert tabular data between formats.' It immediately clarifies the exact operation and gives concrete format-pair examples (CSV to Markdown, Markdown to JSON, HTML table to CSV), which distinguishes it from sibling tools like table_align, table_transform, and table_inspect. The behavior is stated as 'change shape,' making the tool's role 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 explicitly says 'Use this whenever a table needs to change shape,' providing a clear triggering condition. However, it does not mention when not to use it or name alternative sibling tools for alignment or inspection, so the guidance lacks exclusions and alternative routing. This is clear context but not full when/when-not coverage.

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 for behavioral disclosure. It clearly describes what the tool returns (structure and quality metrics) but does not explicitly state that the operation is non-destructive or mention any side effects, error conditions, or permissions. Since it is a 'report' tool, read-only behavior is implied but not stated, leaving a moderate 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 compact and well-organized. It front-loads the purpose, then lists outputs, usage guidance, and parameters. Every sentence contributes value—no filler or redundancy. The flow is logical and easy to parse.

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 is fairly simple (2 params, output schema exists) and the description covers the input semantics, output content, and usage sequence. It lacks explicit error-handling notes (e.g., unsupported formats) or size limitations, but these are minor given the clarity of the rest. The inclusion of the sibling for the next step adds 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?

Schema description coverage is 0%, so the description must compensate. The Args section explains both parameters: 'text: The whole table as text' and 'from_format: How to read the input. markdown, csv, tsv, json or html.' This adds meaning beyond the schema's bare titles and types, though it omits constraints like size limits or encoding, keeping it just shy of a 5.

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 a specific verb ('Report'), resource ('a table'), and enumerates the exact output (row/column counts, per-column summary, list of problems). It also explicitly differentiates from siblings by positioning itself as the first step before table_transform, so an agent can distinguish it without inspecting other 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 gives a clear directive: 'Call this first when a table looks suspicious, then fix it with table_transform.' This specifies when to use the tool and names the alternative for the follow-up action, effectively covering the usage context and a key sibling distinction.

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 of behavioral disclosure. It usefully states that the whole table is processed at once and row order is preserved, but does not mention statelessness, error handling, or data-size limits. Coverage is partial but not misleading.

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

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

The description is front-loaded with a one-line summary, followed by a usage guideline and a structured Args block. Given 13 parameters and no schema descriptions, the detail in the Args block is necessary. Every sentence earns its place; there is no 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 complex tool with 13 parameters and an output schema, the description covers all parameters and operation options, and the output schema handles return-structure explanation. Some edge cases (e.g., empty input, invalid format behavior) are unspecified, but an agent can correctly call the tool with the given information.

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

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

Schema description coverage is 0%, and the description's Args section fully compensates: it explains every parameter, including operation-specific behavior (e.g., 1-based column numbers for select_columns, the operator list for filter, and format options). This goes well beyond the bare schema.

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

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

The description opens with a specific verb ('reshape') and resource ('a table'), and enumerates the supported operations (transpose, select_columns, rename_columns, sort, dedupe, filter). This makes the core purpose clear. It does not explicitly name sibling tools, so differentiation from tools like table_convert or table_align is implicit rather than spelled out.

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 a clear usage context: prefer this over manual row editing for mechanical changes, citing whole-table processing and row-order preservation. It does not, however, mention when to use sibling tools such as table_convert or table_align, leaving alternative selection to inference.

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