Short catalog of common HTTP status codes.
| Name | Required | Description | Default |
|---|---|---|---|
| url | No | HTTPS URL to normalize or cite | |
| host | No | Public hostname | |
| json | No | JSON text to validate; discarded after the check | |
| zone | No | IANA timezone name |
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 disclosing behavior. It only says the tool is a short catalog, which implies a read-only reference but does not state whether it accepts input, makes network calls, returns a list, or has any side effects.
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 short sentence that immediately states the tool's subject. It is efficient and front-loaded, though it is terse enough that it omits important usage and invocation 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?
With no output schema, no annotations, and four optional but seemingly mismatched parameters, the description is incomplete. It does not say what the catalog contains exactly, how the parameters affect results, or what kind of response the agent should expect.
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 even though the description itself adds no parameter meaning. However, the schema's parameter descriptions (URL, hostname, JSON, timezone) appear unrelated to HTTP status codes, which weakens their practical value for an agent trying to invoke this tool.
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 identifies the resource as a catalog of common HTTP status codes, which is a specific subject and distinguishes it from the sibling tools focused on URLs, timezones, and JSON. However, it is a noun phrase rather than a verb-based operation statement, so it does not fully say what the tool does.
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 instead of the sibling tools such as validate-json, normalize-url, or iana-zones. There are no conditions, exclusions, or alternative routing hints.
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.
Most tools are distinct, but there is overlap in the URL/time clusters: citation, normalize-url, and domain-shape all involve host/scheme data, while timezone and utc-time both return current time. The vague compatibility tool also adds ambiguity.
Names mix noun-only forms (citation, timezone), verb-object forms (inspect-robots, normalize-url, validate-json), and compound nouns (iana-zones, status-catalog, utc-time). There is no consistent naming convention across the set.
Ten tools is a reasonable count for a general-purpose utility server. Each tool is lightweight and the set is neither bloated nor too sparse.
The domain is an open-ended 'utility index,' so completeness is hard to define; it covers URL handling, time, HTTP status, robots, and JSON validation. However, obvious common utilities like hashing, encoding, or string operations are absent, leaving notable gaps for a general utility server.