decode_base64
Decode a Base64 string to UTF-8.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | Base64 string to decode |
Decode a Base64 string to UTF-8.
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | Base64 string to decode |
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 are provided, so the description carries the full burden of behavioral disclosure. It mentions the output format (UTF-8) but does not disclose error handling, side effects, or safety profile. While the tool is a simple pure function, the description does not explicitly confirm this, leaving a transparency gap.
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 that conveys the essential action and result without any redundant or extraneous information. It is highly concise and well-structured.
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 tool with one parameter and no output schema, the description is mostly complete. It states the output as UTF-8, implying a string return value. While an explicit return type would improve completeness, the implication is strong enough for this straightforward utility.
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 fully describes the single parameter 'input' with 'Base64 string to decode', providing 100% coverage. The description adds no additional parameter semantics, 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 states 'Decode a Base64 string to UTF-8.' This clearly identifies the tool's purpose with a specific verb (decode), resource (Base64 string), and output format (UTF-8). It is unambiguous and easily distinguished from the sibling encode_base64.
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. It does not reference encode_base64 or other tools, leaving the agent without explicit usage context or exclusions. The absence of any usage direction places this at a score of 2.
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.
Each tool has a distinct purpose and target resource or operation. While some tools are thematically related (e.g., detect_secrets and classify_gdpr both analyze text), their specific outputs and use cases are clearly separated by names and descriptions.
Most tools follow a clear verb_noun pattern (convert_currency, generate_uuid, validate_iban), and the noun_to_noun conversion tools (csv_to_json, html_to_text) form a consistent sub-pattern. The mix of verb_noun and X_to_Y is understandable and predictable, though not uniform.
23 tools is on the higher end for a utility server, feeling like a grab-bag of many unrelated functions. While each tool is simple and serves a purpose, the count exceeds the typical well-scoped range, making it heavier than ideal.
The tool coverage is broad but scattered with no clear domain focus. Obvious complementary utilities are missing, such as URL encoding/decoding, YAML conversion, or PDF generation. However, within each small category, core operations are present, so agents can work around gaps.