text_diffCompute an exact line-based diff between two texts with add/remove/unchanged stats.
| Name | Required | Description | Default |
|---|---|---|---|
| modified | Yes | The text after the change | |
| original | Yes | The text before the change |
| Name | Required | Description | Default |
|---|---|---|---|
| lines | No | ||
| stats | No | ||
| result | No | The result, when it is not an object |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / modified / descriptionAdded value: +"The text after the change"Input schema / properties / original / descriptionAdded value: +"The text before the change"Output schema / (root)Previous value: -nullNew value: +{
+ "$schema": "https://json-schema.org/draft/2020-12/schema",
+ "additionalProperties": {},
+ "properties": {
+ "lines": {
+ "items": {},
+ "type": "array"
+ },
+ "result": {
+ "description": "The result, when it is not an object"
+ },
+ "stats": {
+ "additionalProperties": {},
+ "properties": {
+ "added": {
+ "type": "number"
+ },
+ "removed": {
+ "type": "number"
+ },
+ "unchanged": {
+ "type": "number"
+ }
+ },
+ "type": "object"
+ }
+ },
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as read-only, idempotent, and non-destructive. The description adds useful behavioral detail beyond those annotations by specifying that the diff is exact, line-based, and produces add/remove/unchanged statistics. No contradiction exists.
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 sentence that front-loads the core action and result. It contains no redundant phrasing or extraneous information.
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 two-parameter, read-only utility with a complete input schema and an output schema, this description covers the essential behavior: line-based diffing and the statistics returned. Nothing critical is missing.
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 already documents both parameters, original and modified, with clear descriptions, so schema coverage is essentially complete. The tool description adds no parameter-specific semantic detail beyond the general line-based nature of the operation.
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, 'Compute', and names the exact resource and granularity: a line-based diff between two texts with add/remove/unchanged stats. This clearly distinguishes it from the listed sibling tools, none of which perform diffing.
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 intended use is implied: an agent would use this when an exact line-level comparison of two texts is needed. However, there is no explicit when-not-to-use guidance or mention of alternatives such as word-level or fuzzy diffing.
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.
Every tool targets a distinct resource or action, and the detailed descriptions clearly separate near neighbors like generate_test_bsn versus generate_brp_test_data, read_page versus url_screenshot versus url_to_pdf, and image_compress/convert/resize. Even with 40 tools, there is no real boundary-blurring overlap.
All names are snake_case and readable, but the set mixes conventions: verb_noun (generate_*, validate_*), noun_verb (pdf_merge, image_resize), conversion-style names (csv_to_json, html_to_pdf), and bare nouns (base64, qr_code_png). The groups are recognizable, but there is no single predictable pattern.
Forty tools is an oversized surface for an agent to consider on every call, well above the point where tool selection cost starts to hurt. The broad purpose explains the count, but many one-off utilities could be grouped or exposed selectively.
The server covers many domains—encoding, Dutch test data, image/PDF handling, memory, and workflows—but several categories are partial: there are no reverse conversions like json_to_csv or html_to_markdown, no PDF text extraction, and no workflow create/update/delete tools. Agents can work around some gaps, but notable operations are missing.