base64Encode text to Base64 or decode Base64 to text, byte-exact.
| Name | Required | Description | Default |
|---|---|---|---|
| mode | Yes | encode turns text into Base64; decode turns Base64 back into text | |
| text | Yes | The text to encode, or the Base64 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 turns text into Base64; decode turns Base64 back into text"Input schema / properties / text / descriptionAdded value: +"The text to encode, or the Base64 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?
The phrase 'byte-exact' is a useful behavioral guarantee beyond the readOnlyHint and idempotentHint annotations; it signals that the transformation preserves bytes without charset conversion or normalization. It does not contradict the annotations, and the read-only/idempotent traits are already covered by annotations.
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?
One compact sentence states the full operation and adds the meaningful 'byte-exact' qualifier. There is no redundant wording or filler; the most important information is front-loaded.
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 utility with full schema coverage, annotations, and an output schema, the description is nearly complete. It could mention failure behavior for invalid Base64 input or padding requirements, but these are minor gaps for such a straightforward tool.
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 description coverage is 100%, and both parameters are already well documented: 'mode' enumerates encode/decode and 'text' explains its role. The description adds no additional parameter-level detail beyond the schema, 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 identifies the exact operation: encode text to Base64 or decode Base64 back to text. The verb pair 'encode/decode' plus the resource 'Base64' makes the tool's purpose immediately obvious and distinguishes it from siblings like url_encode.
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 clear context for when to use the tool: whenever Base64 encoding or decoding is needed. It does not explicitly name alternatives or exclusion criteria, but the operation is specific enough that an agent can confidently select 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.
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.