random_string
Random string (alphanumeric, hex, base64 charset or custom).
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| seed | No | ||
| length | No | ||
| charset | No | alphanumeric |
Random string (alphanumeric, hex, base64 charset or custom).
| Name | Required | Description | Default |
|---|---|---|---|
| seed | No | ||
| length | No | ||
| charset | No | alphanumeric |
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?
With no annotations provided, the description must fully disclose behavioral traits. It only lists supported charsets but does not mention whether the generation is cryptographically secure, the role of the seed parameter, or any side effects. This is insufficient for an agent to use the tool correctly.
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 sentence with no extraneous words. It is efficiently front-loaded and earns its place by adding specific charset information.
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 no annotations, no output schema, and 3 parameters with 0% schema description coverage, the description is too minimal. It omits details about output format, default behavior, seed usage, and custom charset format, making it incomplete for an agent to rely on.
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 description adds meaning for the charset parameter by listing example values (alphanumeric, hex, base64, custom), which goes beyond the schema's default. However, it does not explain the seed or length parameters, and schema description coverage is 0%, so it only partially compensates.
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 'Random string' and lists the supported charsets (alphanumeric, hex, base64, custom), which clarifies the resource and scope. However, it does not explicitly say it generates a random string, leaving some room for ambiguity. Still, it effectively distinguishes from siblings like password_generate or nanoid_generate by specifying charset options.
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 no guidance on when to use this tool versus alternatives such as password_generate or nanoid_generate. It fails to mention use cases, limitations, or exclusions, leaving the agent to infer usage from the name alone.
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 has a clear, distinct purpose with thorough descriptions. Even closely related tools like base64_decode/encode and hash_md5/sha256 are easily differentiated by name and description.
All tools follow a consistent lowercase_underscore naming convention, typically in a <domain>_<action> or <action>_<domain> pattern. There are no jarring deviations or mixed styles.
193 tools is an extreme count, far beyond what any focused server needs. While each tool has utility, the sheer number creates a kitchen-sink effect that overwhelms agents and hinders discoverability.
Within each subdomain (JSON, cron, JWT, etc.), the coverage is exhaustive, covering validation, conversion, parsing, and more. Minor gaps exist (e.g., YAML-to-TOML conversion missing), but overall it is remarkably complete.