url_encodeURL-encode or decode a string with exact percent-encoding.
| Name | Required | Description | Default |
|---|---|---|---|
| mode | Yes | encode or decode | |
| text | Yes | The text to percent-encode, or the encoded string to decode |
| Name | Required | Description | Default |
|---|---|---|---|
| action | No | ||
| output | No | The final output of the workflow | |
| 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 / mode / descriptionAdded value: +"encode or decode"Input schema / properties / text / descriptionAdded value: +"The text to percent-encode, or the encoded string to decode"Output schema / (root)Previous value: -nullNew value: +{
+ "$schema": "https://json-schema.org/draft/2020-12/schema",
+ "additionalProperties": {},
+ "properties": {
+ "action": {
+ "type": "string"
+ },
+ "output": {
+ "description": "The final output of the workflow",
+ "type": "string"
+ },
+ "result": {
+ "description": "The result, when it is not an object"
+ }
+ },
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, covering safety and repeatability. The description adds 'exact percent-encoding,' which hints at strict encoding behavior, but it does not clarify edge-case behavior such as malformed percent sequences during decode or which characters are encoded.
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, front-loaded sentence with no redundant words. It states both the action and the key precision qualifier efficiently, making it easy for an agent to parse.
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 pure transformation tool, the description, schema, and annotations cover most of what an agent needs: parameters are documented, the operation is read-only and idempotent, and the return value is implicitly the encoded/decoded string. A slightly fuller mention of return behavior or invalid-input handling would make it fully complete.
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 for both parameters is essentially 100%: 'mode' is explained as 'encode or decode' and 'text' is described as the string to encode/decode. The description adds no substantive parameter meaning beyond what the schema already provides, so the baseline of 3 applies.
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 specific action ('URL-encode or decode') and the resource ('a string'), and adds the meaningful qualifier 'with exact percent-encoding.' This distinguishes it from sibling transformation utilities and makes the tool's purpose immediately understandable.
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 no guidance on when to use this tool versus alternatives, nor does it mention any exclusions or prerequisites. An agent must infer applicability purely from the tool name and description, with no explicit routing to or away from sibling tools.
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.