Fetch a public robots.txt and return group counts only.
| 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?
With no annotations provided, the description carries the full burden of disclosing behavior. Instead of stating what actions the tool performs, it describes an environment and links to external docs. The actual behavior remains completely hidden, and the reference to 'global reasoning or memory' adds confusion rather than clarity.
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 run-on sentence that mixes unrelated concerns: execution environment, documentation links, variant mentions, and a platform URL. It is neither concise nor structured, and the important information (what the tool does) is absent.
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 tool has four parameters, no output schema, and no annotations. A complete description would explain what the tool does with these parameters and what the result is. The description instead discusses a runtime and external documentation, leaving an agent without the basic context needed to invoke the tool correctly.
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. The description adds no parameter-level information, but since the schema already documents all four parameters, the description does not need to compensate. There is no contradiction or added value.
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 does not state what the tool does. It describes a runtime environment ('Run inside a subprocess once a code snippet is accepted') and references OpenAI documentation and a platform URL, but never specifies the tool's operation or resource. This is misleading and fails to distinguish the tool from any sibling.
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 guidance is provided on when to use this tool vs alternatives. The description vaguely mentions 'architecture-constrained runtime' and 'fallback variants' but does not explain which variant to choose or under what conditions. This is effectively no usable usage guidance and could mislead an agent.
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 distinct outputs, but there are overlapping clusters: citation and normalize-url both return host information, timezone/utc-time/iana-zones all revolve around time, and domain-shape/punycode-ok both target hostnames. An agent would need to read descriptions carefully to avoid picking the wrong one.
Naming style is inconsistent: some names are verbs (normalize-url, validate-json, inspect-robots), some are nouns (citation, compatibility, timezone), and some are quirky compounds (punycode-ok, iana-zones). The consistent lowercase/hyphen formatting is the only pattern.
Eleven tools is not an unreasonable count by itself, but the server is named for punycode host checking while many tools are unrelated URL, time, and JSON utilities. The set feels padded rather than deliberately scoped.
The tool surface has no coherent domain to be complete against: a punycode host checker would need at least IDN conversion or comparison, while the URL/time/JSON utilities appear as unrelated extras. Significant coverage gaps would likely force agents to combine or externalize work.