Generate RFC 4122 version 4 UUIDs and/or cryptographically random URL-safe tokens. Batch count is clamped to 1-10.
| Name | Required | Description | Default |
|---|---|---|---|
| kind | No | uuid = v4 UUIDs, token = random base64url tokens, both = pairs | uuid |
| count | No | How many to generate (clamped 1-10) |
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 zero annotations provided, the description carries the full burden of behavioral disclosure. It discloses the exact UUID standard (RFC 4122 v4), the randomness source (cryptographically secure), the token encoding (URL-safe base64url), and the effective batch cap (clamped to 1-10) — the clamping note being genuinely valuable since the schema's min/max of ±9e15 would otherwise mislead. It stops short of stating the output shape (single value vs array, pair structure for 'both') or error behavior for out-of-range counts.
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?
Two sentences with zero filler, front-loaded with the primary action and format details in the first sentence. The clamping disclosure in the second sentence earns its place by flagging a runtime constraint an agent must respect when choosing batch sizes.
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 low-complexity tool (2 optional params, no output schema), the description covers purpose, format, and clamping, but omits the return shape — a real gap since an agent cannot know whether count=1 yields a bare string or an array, or what 'pairs' looks like for kind='both'. It also leaves out-of-range count behavior ambiguous: the schema permits negatives and enormous values, and the description does not clarify whether those error out or are silently clamped.
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%, so the baseline is 3. Both parameters are already documented in the schema — 'kind' has an enum with per-value descriptions and 'count' carries its own '(clamped 1-10)' note — so the free-text description adds no meaning beyond what the schema already provides.
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?
States a specific verb ('Generate'), resource ('RFC 4122 version 4 UUIDs and/or cryptographically random URL-safe tokens'), and a clear scope. This distinguishes it from all 15 siblings — none generate random identifiers, and the closest sibling hash_compute clearly computes hashes from user input rather than minting values. An agent can select this tool without opening the schema.
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 when-to-use or when-not-to-use guidance is given — no alternatives are named and no use cases are described. Usage is implied rather than stated: the purpose is unambiguous and no sibling functionally overlaps, so misrouting is unlikely. However, the description itself earns no credit for stating any selection criteria.
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 targets a distinct operation—encoding, color conversion, parsing, hashing, JWT validation, Markdown rendering, regex testing, SemVer operations, diffing, URL analysis, and UUID generation. The four SemVer tools are related but cleanly separated by action (bump vs compare vs max vs satisfies), and descriptions clarify their boundaries.
Tools overwhelmingly follow an object_verb snake_case convention (base64_encode, csv_parse, regex_test, semver_bump). Semver_max and semver_satisfies deviate slightly from the imperative verb pattern, but the overall naming is predictable and searchable.
At 16 tools, the server is slightly above the ideal 3–15 tool range but each utility earns its place for a general-purpose developer toolbox. No tools feel redundant, and the count remains manageable because the names and domains are highly scannable.
The toolkit covers a solid breadth of common developer utilities: encodings, common formats (JSON, CSV, Markdown), hashing/JWT, regex, SemVer, cron, URL, UUIDs, and diffing. It lacks some fringe converters such as YAML/XML parsing or HTML escape/unescape, but these are minor gaps that agents can work around rather than dead ends.