text_diffLine-level diff of two texts (LCS): counts of added/removed lines plus a marked-up line list. Runs on smart-tools.xyz.
| Name | Required | Description | Default |
|---|---|---|---|
| a | Yes | First (original) text | |
| b | Yes | Second (changed) text | |
| trim | No | Trim each line before comparing (default true) | |
| locale | No | Language for the source_url link (default en) | |
| ignore_case | No | Compare case-insensitively (default false) |
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 the core algorithm (LCS) and the output format, which is useful. However, it does not mention how optional parameters (trim, ignore_case) affect behavior, nor edge cases like line endings or whitespace. The phrase 'Runs on smart-tools.xyz' is irrelevant to 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 first sentence is concise and packs meaningful detail (algorithm, scope, outputs). However, the second sentence 'Runs on smart-tools.xyz' is filler that does not contribute to usage or understanding, 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 tool with 5 parameters and no output schema, the description adequately explains the primary purpose and return value. It does not explain how locale affects the 'source_url' link or the exact format of the marked-up list, but these are secondary given the schema provides parameter details. Overall, it is sufficient for an AI agent to 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?
The input schema has 100% parameter coverage with clear descriptions, so the baseline is 3. The description adds no additional semantic detail beyond referring to 'two texts', which maps to parameters a and b. It does not explain the locale parameter or its purpose (source_url), but the schema already covers these.
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 performs a line-level diff of two texts using LCS, with specific outputs (counts of added/removed lines and a marked-up line list). This distinguishes it from all sibling tools, which are calculators/converters or formatters.
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?
No guidance is given on when to use this tool versus alternatives. It does not mention any exclusions, prerequisites, or situations where another tool would be more appropriate. The description only states what it does, not when to invoke it.
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.