markdown_tableBuild a padded GitHub-flavored Markdown table from CSV/TSV text (first row is the header). Runs on smart-tools.xyz.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | CSV/TSV rows; the first row is the header | |
| 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 carries the transparency burden. It discloses two behavioral traits: the table is 'padded' and the first row is treated as a header. It also notes it 'Runs on smart-tools.xyz', which is context but not critical. It does not mention whether the operation is read-only, possible side effects, or error handling, but given the tool's simple nature, this is acceptable.
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 two sentences and front-loaded with the core action. The first sentence is essential; the second ('Runs on smart-tools.xyz') is somewhat unnecessary for tool selection but is short and does not seriously distract. It is concise and structured well.
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 two parameters (one required), the description covers the essential behavior: input format, transformation, and key assumption. The output schema is absent, but the output is obvious from the purpose. The locale parameter's meaning is vague in the schema, yet the description does not need to explain it further. Overall, the description is sufficiently complete for this 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 description covers both parameters (text and locale) at 100%, so the description need not repeat them. The description adds the detail that input is CSV/TSV and first row is header, which reinforces the schema's 'text' parameter but provides no additional meaning beyond that. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Build') and resource ('padded GitHub-flavored Markdown table') and clearly states input format ('CSV/TSV text') with a key assumption ('first row is the header'). It differentiates well from sibling tools like csv_to_json or table_to_csv by focusing on Markdown output.
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 when to use the tool: when you have CSV/TSV text and need a Markdown table. However, it does not explicitly mention alternatives or when not to use it, unlike high-quality examples that name sibling tools. It provides enough context to infer the use case but lacks explicit exclusions or comparisons.
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.