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TOML table count, body discarded

web-fetch

HTTP status and content type for a public URL. Body discarded.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
refNoGit ref name; discarded after the shape check
urlNoHTTPS URL to normalize or cite
cityNoCity name for a public weather hint; discarded after the call
feedNoPublic RSS or Atom URL; titles discarded
hostNoPublic hostname
jsonNoJSON text to validate; discarded after the check
pathNoFile path to check; no disk access
zoneNoIANA timezone name
queryNoSearch text; discarded after the length check

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

C2.4/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses one behavioral trait: 'Body discarded', which is useful. However, with no annotations provided, the description carries the full burden. It does not mention error handling, redirects, timeouts, authentication requirements, or what happens when non-URL parameters are passed. Given the tool has 9 parameters with widely varying behaviors, this is a significant transparency gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise – a single sentence with no wasted words. It front-loads the core action and output. However, given the complexity of 9 parameters, this brevity sacrifices necessary detail, so it is not perfect.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 9 parameters, 0 required, no output schema, and no annotations, the description is far too thin. It only addresses the 'url' use case and ignores the other parameters, which appear to serve entirely different purposes (weather hints, JSON validation, timezone checks, etc.). An agent cannot infer how the tool behaves when multiple parameters are present or how to interpret results for non-URL inputs.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% – each parameter has a description in the schema (e.g., 'HTTPS URL to normalize or cite', 'City name for a public weather hint; discarded after the call'). The tool description adds no additional parameter-level meaning beyond what the schema already provides, so it earns the baseline of 3 for a well-covered schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific output (HTTP status and content type) and a specific resource (public URL), which is clear for the 'url' parameter. However, the input schema includes 8 other parameters (ref, city, feed, host, json, path, zone, query) that are not mentioned in the description, making the tool's overall purpose ambiguous. It reads as if the tool only handles URLs, but it appears to be a multi-purpose dispatcher.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus its siblings. There is no mention of when to prefer web-fetch over fetch-status, browser-url-ok, or normalize-url, nor any exclusions or prerequisites. The agent must infer usage from the schema alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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