Compute a cryptographic hash of input data. Supports MD5, SHA1, SHA256, and SHA512. Returns the hex digest.
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | The data to hash | |
| algorithm | No | Hash algorithm to use | sha256 |
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, the description carries the full behavioral burden. It discloses that the operation is a pure computation, returns a hex digest, and supports several algorithms. It could mention input encoding, but for a simple utility this is adequate.
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 compact sentences convey purpose, supported algorithm choices, and output format without redundancy. The most important information is front-loaded.
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 description is sufficient for a simple two-parameter tool: it states the input, algorithm choices, and return format. Minor specifics such as input encoding and the default algorithm are absent, but the schema covers the default and the rest is reasonably inferable.
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 schema already documents both 'input' and 'algorithm'. The description adds no extra meaning about parameter formats or constraints beyond confirming the algorithm options.
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 opens with a specific action and resource: 'Compute a cryptographic hash of input data.' It then lists supported algorithms and the output format, making the tool's identity unmistakable and clearly distinct from sibling utilities like base64_encode.
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 clear context: this tool is for computing cryptographic hashes. It does not explicitly discuss when-not-to-use or compare to alternatives, but with no hash-related sibling in the list, the intended usage is evident.
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.