http_headers_fetch
Fetch HTTP response headers (HEAD/GET) for URL.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| method | No | HEAD | |
| follow_redirects | No |
Fetch HTTP response headers (HEAD/GET) for URL.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| method | No | HEAD | |
| follow_redirects | No |
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 must disclose behavioral traits. It does not mention side effects (network request, potential timeouts, rate limits), error handling, or the output format (what headers are returned). The description is insufficient for understanding the tool's behavior.
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 very concise (one sentence) but lacks structure. It does not front-load critical details like defaults. While brevity is valued, more information could be added without significant bloat.
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?
Given no output schema, no annotations, and 0% parameter documentation, the description is severely incomplete. It fails to explain what the tool returns, how to handle errors, or how parameters affect 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?
With 0% schema description coverage, the description must explain parameters. It only hints at the 'method' parameter by mentioning HEAD/GET, but does not describe the 'url' or 'follow_redirects' parameters. Parameter meaning and defaults are missing.
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 the tool's action (Fetch), resource (HTTP response headers), and scope (for URL). It also mentions the available methods (HEAD/GET), making the purpose specific and distinguishable from related siblings like http_headers_parse.
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 on when to use this tool versus alternatives, prerequisites (e.g., URL validity, network access), or when to choose HEAD vs GET. The description lacks context for appropriate invocation.
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.
Every tool has a clear, distinct purpose with thorough descriptions. Even closely related tools like base64_decode/encode and hash_md5/sha256 are easily differentiated by name and description.
All tools follow a consistent lowercase_underscore naming convention, typically in a <domain>_<action> or <action>_<domain> pattern. There are no jarring deviations or mixed styles.
193 tools is an extreme count, far beyond what any focused server needs. While each tool has utility, the sheer number creates a kitchen-sink effect that overwhelms agents and hinders discoverability.
Within each subdomain (JSON, cron, JWT, etc.), the coverage is exhaustive, covering validation, conversion, parsing, and more. Minor gaps exist (e.g., YAML-to-TOML conversion missing), but overall it is remarkably complete.