write_xlsxBuild an Excel .xlsx file from rows (CSV text or JSON arrays), optionally several sheets at once. Numbers are written as real numbers so they sum in Excel, while values with leading zeros stay text so IDs and postcodes survive the round trip.
| Name | Required | Description | Default |
|---|---|---|---|
| url | No | ||
| text | No | ||
| sheets | No | ||
| sheet_name | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden and adds useful, non-obvious behavior: numbers are written as real numbers for Excel summation, while leading-zero values remain text to preserve IDs and postcodes. It does not detail output paths or overwrite behavior, but it meaningfully discloses data-handling semantics.
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 well-structured: the first sentence states the core operation and input options, while the second adds a relevant and non-obvious type-preservation detail. There is no redundant or filler content.
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 high-level purpose and type behavior are clear, but the description is not complete enough for reliable invocation given 4 undocumented parameters, zero schema descriptions, no annotations, and no output schema. It must specify how url, text, sheets, and sheet_name are meant to be used.
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 parameters are entirely opaque. The description hints that text holds CSV content and that sheets supports multi-sheet writes, but it does not explain url, sheet_name, or how sheets and sheet_name relate. An agent would have to guess critical invocation details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: building an Excel .xlsx file from CSV text or JSON arrays, with optional multi-sheet support. It uses a specific verb and resource, and it is clearly distinct from siblings like read_xlsx and the various CSV conversion 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 use context: use this when you need to produce an .xlsx file from row-like data, possibly with multiple sheets. It does not explicitly list when-not-to-use scenarios or alternative tools, so it stops short of full guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clearly distinct functions (conversions, cleaning, xlsx I/O, reconciliation), but diff_tables, reconcile_ledger, and match_transactions all involve comparing or matching records, which could cause initial confusion. However, each has a specific use case—generic column diff, amount-focused reconciliation, and keyless fuzzy matching—and the descriptions provide enough detail to disambiguate them.
The conversion tools follow a consistent 'csv_to_*' or 'json_to_csv' pattern, while operation tools use a verb_noun style (e.g., clean_table, merge_tables, reconcile_ledger). This dual pattern is predictable by function type, but 'what_can_you_do' breaks convention as a question-like meta-tool, so the naming is mostly consistent with a minor deviation.
With 15 tools, the server sits at the upper edge of the well-scoped range, but each tool earns its place in the CSV/spreadsheet domain—covering conversions, cleaning, merging, diffing, reconciliation, matching, and Excel I/O. The count feels appropriate for the server's broad yet focused scope.
The toolset covers the full lifecycle of table manipulation: reading (read_xlsx), encoding repair, cleaning and transforming (clean_table), merging (merge_tables), comparing (diff_tables, reconcile_ledger), matching without keys (match_transactions), entity deduplication, and output to various formats (JSON, MD, chart, QBO, XLSX). No obvious gaps are evident for common tasks.