Show how this request is classified. No identifiers are retained.
| 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 exist, so the description carries the behavioral burden. It adds one genuinely useful trait ('No identifiers are retained'), disclosing a data-retention guarantee. However, it does not say whether the tool makes network calls, is read-only, or produces any side effects, leaving significant behavioral detail undisclosed.
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 short sentences with no filler; the main action is front-loaded and the retention guarantee is a compact second sentence. The vague term 'classified' reduces information density slightly, but there is no wasted text.
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 four-parameter tool with no annotations and no output schema, the description is thin. It does not clarify what the classification output looks like, which parameters are actually relevant, or what 'this request' refers to. An agent would likely need to invoke the tool experimentally to understand its behavior.
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?
All four parameters (url, host, json, zone) are fully documented in the schema with 100% coverage, so the baseline of 3 applies. The description adds no per-parameter semantics, such as which inputs are required, mutually exclusive, or intended for different classification scenarios.
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 verb and object ('Show how this request is classified'), but the classification dimension is never defined — it could refer to compatibility, format, or policy category. The name 'compatibility' hints at one dimension while the description says 'classified,' which is ambiguous, and there is no differentiation from sibling tools like citation or domain-shape.
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 when-to-use or when-not-to-use guidance appears. The description never states what inputs or scenarios should trigger this tool versus its siblings, nor whether it should be tried first. An agent is left to infer its role 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.
Most tools have clearly different outputs, but there are overlapping clusters: citation, normalize-url, and domain-shape all deal with URL/hostname shapes, while timezone, utc-time, and iana-zones all cover time. A couple of vague names like compatibility and citation could also cause initial misselection.
All names are lowercase hyphenated, which gives a visual consistency, but they mix verb-led names like normalize-url and validate-json with noun-led names like status-catalog and timezone. The pattern is readable but not predictable enough to be considered strongly consistent.
11 tools is within a reasonable range and none feels redundant, but the collection spans several unrelated concerns such as URL handling, HTTP status, time, JSON validation, and robots.txt. It is slightly broad, though not bloated.
The set covers common lookups like URL normalization, status codes, time, and JSON validation, but there is no coherent domain that ties these together. Obvious adjacent operations such as redirect checking, response header inspection, or rate-limit-specific details are missing, leaving an agent with possible dead ends.