HTTP Headers
lookup_headersFetch HTTP response headers for a URL. Use when inspecting server configuration, security headers, or caching policies.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
lookup_headersFetch HTTP response headers for a URL. Use when inspecting server configuration, security headers, or caching policies.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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?
Annotations already declare readOnlyHint: true and openWorldHint: true, so the safety profile is covered. The description adds that it fetches headers for a URL, but it does not disclose details like redirect behavior, response size limits, or authentication requirements. It provides some context but is not rich in behavioral traits.
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 concise at two sentences, with the primary action front-loaded. Every sentence adds value: the first defines the action and resource, the second gives concrete use cases. No wasted words.
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 no output schema, so the description could explain what the response contains, but it only says 'fetch HTTP response headers' without detailing the response format. Also, the empty schema and lack of URL parameter create ambiguity about how the target URL is specified. Annotations cover safety, but the description is incomplete for a practical tool.
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?
The tool has zero parameters, and the schema is empty. Per rubric, 0 params is a baseline of 4. The description mentions 'for a URL' but does not specify how the URL is provided, which is a minor ambiguity, but since there are no parameters to document, the baseline holds.
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 states 'Fetch HTTP response headers for a URL' with a specific verb and resource. It implicitly distinguishes from sibling lookup_* tools by focusing on HTTP headers, but it does not explicitly name alternatives or contrast with other tools.
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 provides explicit use cases: 'inspecting server configuration, security headers, or caching policies.' It gives context for when to use the tool, though it does not mention when not to use it or list alternative tools.
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.
The tools are grouped into clear categories (dev, lookup, security, text, transform), which helps with disambiguation, but within categories there is some overlap. For example, lookup_ssl and lookup_ssl_cert_expiry both handle SSL certificates, and dev_url_encode/dev_url_decode are closely related but distinct. Most tools have unique purposes, but a few could be confused without careful reading of descriptions.
The naming follows a consistent snake_case pattern with a clear prefix structure (dev_, lookup_, security_, text_, transform_), which aids in organization. However, there are minor deviations like dev_cron_describe using 'describe' while others use verbs like 'generate' or 'convert', and some tools have longer names that break the verb_noun pattern slightly. Overall, the naming is predictable and readable.
With 49 tools, the count is excessive for a utility server, making it overwhelming and likely to cause confusion or inefficiency. While the tools cover many use cases, a more focused set of 15-25 tools would be more manageable and better scoped. The high number suggests feature bloat rather than a coherent, minimal surface.
The tool set is highly complete for its utility and development support domain, covering a wide range of operations from data transformation and security to lookups and text processing. There are no obvious gaps; each category provides comprehensive coverage, such as full text encoding/decoding, security functions, and various lookup capabilities, ensuring agents can handle diverse tasks without dead ends.