table_to_csvConvert a pasted HTML or delimited (tab/semicolon/comma) table into clean CSV. Runs on smart-tools.xyz.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | An HTML <table> or delimited table text | |
| locale | No | Language for the source_url link (default en) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry behavioral disclosure. It adds that output is 'clean CSV' and specifies supported delimiters (tab/semicolon/comma), which informs behavior. However, it does not mention side effects, output format details, or the purpose of the locale parameter's link to source_url.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that front-loads the tool's action. The inclusion of 'Runs on smart-tools.xyz' adds little functional value but does not detract significantly 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?
For a simple tool with no output schema, the description should explain the return value enough for an agent. It says output is CSV, but not how it is returned (e.g., plain text). The locale parameter's role is unexplained, leaving a gap in understanding the full tool behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3. The description adds specificity by enumerating delimiters (tab/semicolon/comma), which goes beyond the schema's generic 'delimited'. However, it does not clarify the locale parameter, which remains ambiguous.
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 'Convert' with a clear resource (pasted HTML <table> or delimited text) and output (clean CSV). This distinguishes it from siblings like csv_to_json and csv_to_sql which operate in the opposite direction.
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 clearly states the input and output, implying the use case: when you have an HTML table or tab/semicolon/comma delimited text and need CSV. It does not explicitly name alternative tools or exclusions, but the context is clear.
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 are clearly distinct, but there are notable overlaps. 'hash_generator' and 'file_hash' effectively do the same thing (SHA hashing of text), and several CSV/TSV converters (csv_to_json, csv_to_sql, table_to_csv, markdown_table) have similar input handling, though outputs differ. The sheer number of converters is clear, but these near-duplicates create some ambiguity.
Names follow a generally consistent pattern: lowercase with underscores, often ending in 'converter', 'calculator', or 'generator'. A few names like 'base64', 'lorem_ipsum', and 'transliteration' deviate from the suffix pattern, but the naming style is uniform and predictable overall.
With 73 tools, this server is far over the typical well-scoped range. While the broad utility-toolkit purpose might justify a larger set, 73 is unwieldy and pushes well into 'too many' territory, making it harder for an agent to select the right tool efficiently.
The server covers a remarkably wide range of common utilities: unit conversions, calculators, text transformations, development helpers (JSON, regex, JWT, subnet), and generators. Minor gaps exist (e.g., currency converter, time zone converter), but for a general-purpose toolkit, it is largely comprehensive.