text_base64_decodeDecode a Base64 string back to UTF-8 text. Use when extracting data from Base64-encoded API responses, tokens, or email headers. Returns the original plaintext string.
| Name | Required | Description | Default |
|---|---|---|---|
| encoded | Yes | The Base64 string to decode | |
| urlSafe | No | Input uses URL-safe alphabet |
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?
The annotation readOnlyHint: true already establishes that this is a safe, read-only operation. The description adds a modest amount of context by stating the return value ('Returns the original plaintext string') and specifying UTF-8 as the output encoding. It doesn't disclose edge-case behavior (e.g., invalid base64 handling), but given the annotation coverage, this is acceptable.
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 two sentences long and front-loaded with the core action. It immediately states what the tool does, then provides a use case and return value, with zero wasted words. Every sentence serves a purpose.
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, read-only utility with only two parameters, the description is complete enough. It covers the action, use cases, and return value. There is no output schema, so the stated return value is useful. It doesn't address error conditions, but that's not critical for this tool's simplicity and the annotation already signals safety.
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%, meaning both parameters ('encoded' and 'urlSafe') are already fully described in the input schema. The description adds no additional parameter-level detail, so the baseline score of 3 is appropriate. The schema carries the load here.
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 tool's action: 'Decode a Base64 string back to UTF-8 text.' This is a specific verb+resource combination that accurately distinguishes it from the sibling tool text_base64_encode. The purpose is unambiguous and 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 provides explicit use cases: 'Use when extracting data from Base64-encoded API responses, tokens, or email headers.' This gives clear context for when the tool is appropriate. However, it doesn't explicitly mention when not to use it or name alternatives like text_base64_encode as a contrast, so it falls short of a 5.
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.
The tools are grouped into clear categories (dev, lookup, security, text, transform), which helps with disambiguation, but within categories there is some overlap. For example, lookup_ssl and lookup_ssl_cert_expiry both handle SSL certificates, and dev_url_encode/dev_url_decode are closely related but distinct. Most tools have unique purposes, but a few could be confused without careful reading of descriptions.
The naming follows a consistent snake_case pattern with a clear prefix structure (dev_, lookup_, security_, text_, transform_), which aids in organization. However, there are minor deviations like dev_cron_describe using 'describe' while others use verbs like 'generate' or 'convert', and some tools have longer names that break the verb_noun pattern slightly. Overall, the naming is predictable and readable.
With 49 tools, the count is excessive for a utility server, making it overwhelming and likely to cause confusion or inefficiency. While the tools cover many use cases, a more focused set of 15-25 tools would be more manageable and better scoped. The high number suggests feature bloat rather than a coherent, minimal surface.
The tool set is highly complete for its utility and development support domain, covering a wide range of operations from data transformation and security to lookups and text processing. There are no obvious gaps; each category provides comprehensive coverage, such as full text encoding/decoding, security functions, and various lookup capabilities, ensuring agents can handle diverse tasks without dead ends.