Encode text to Base64 or decode a Base64 string back to text.
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | Either "encode" or "decode". Defaults to "encode". | encode |
| text | Yes | The text to encode, or the Base64 string to decode. |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description should disclose behavioral traits. It only states the basic operation without mentioning error handling (e.g., invalid Base64 input) or character encoding. Minimal disclosure beyond the 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?
Single sentence, no waste. Front-loaded with the core purpose. Every word earns its place.
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?
The tool has a simple purpose and output schema exists, so the description is adequate but could mention that output is the encoded/decoded string. No mention of edge cases or transformations.
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 is 100%, so the description adds no new meaning beyond what the input schema already provides. Given high coverage, baseline 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 clearly states the verb (encode/decode) and resource (Base64), distinguishing between two modes. It is specific and not tautological.
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 explicit guidance on when to use this tool versus alternatives like hash_text. The mode parameter implies when to encode vs decode, but no direct 'when-to-use' or 'when-not-to-use' advice.
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.
All seven tools have clearly distinct purposes: arithmetic, date operations, timestamp conversion, unit conversion, Base64 encoding, currency exchange, and hashing. No two tools overlap in functionality, making disambiguation straightforward.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., convert_units, calculate_date). The names are descriptive and predictable, with the only exception being 'calculate' which is a single verb but still fits the pattern.
With 7 tools, the server is well-scoped for a utility converter. Each tool addresses a distinct need (math, dates, timestamps, units, encoding, exchange rates, hashing) without unnecessary redundancy or excessive complexity.
The tool set covers common utility conversions and calculations, including arithmetic, dates, units, timestamps, encoding, exchange rates, and hashing. Minor gaps exist (e.g., no string or color conversion), but the core workflows are well-supported and functional.