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?
Annotations already cover the safety profile (read-only, idempotent, non-destructive). The description adds 'byte-exact', which is a meaningful behavioral guarantee about losslessness beyond the annotations. It does not mention error behavior for invalid Base64 input, but this is a minor gap for such a simple pure function.
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?
A single sentence that front-loads the core operation and includes the important byte-exact caveat. Every word earns its place, with no redundancy or fluff.
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?
Given the tool's low complexity, fully annotated safety profile, complete parameter schema, and presence of an output schema, the description is sufficient. It covers both encode and decode modes and the byte-exact guarantee, leaving no critical information missing for correct invocation.
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%, with both parameters already well-documented (mode enum meanings and text purpose). The description does not add parameter-specific details beyond the schema, so the baseline score of 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 explicitly states the tool's action ('Encode text to Base64 or decode Base64 to text') with a specific resource and operation. It clearly distinguishes itself from siblings like url_encode by naming the exact Base64 format, and adds the byte-exact guarantee to the purpose.
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 required. It does not explicitly name alternatives or exclusions, but the tool's purpose is so unambiguous that an agent can easily select it over siblings without additional guidance.
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 target a clearly distinct resource and action, so an agent can usually tell them apart. A few close pairs exist (generate_test_bsn vs generate_brp_test_data, html_to_pdf vs url_to_pdf, read_page vs url_screenshot), but the descriptions draw clear boundaries.
Tool names consistently use snake_case and mostly follow a verb_noun pattern like generate_slug, validate_email, or pdf_merge. There are a few noun-style exceptions such as base64, csv_to_json, and password_strength, but no mixed casing or chaotic naming.
40 tools is heavy and exceeds the comfortable selection range for most agents. The server presents itself as a general-purpose utility toolbox, so the breadth is defensible, but the large flat tool list creates real navigation burden.
The toolkit covers common encoding, conversion, image/PDF, validation, Dutch-specific test data, memory, and workflow needs quite well. Minor gaps like PDF text extraction or JSON-to-CSV conversion exist, but agents can typically work around them.