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Milliliters to teaspoons

search-query-len

Count characters in a search query. Query 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

D1.5/5.0
Behavior1/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only says 'Query discarded,' which is vague and does not clarify side effects, network access, return values, or which parameters are actually used. The schema hints at side-effect-free checks (e.g., 'no disk access'), but the description itself adds no behavioral context.

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

Conciseness2/5

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

The description is extremely short (two sentences), but it is under-specified rather than concise. It omits essential information about the tool's operation and the role of its parameters. The structure does not front-load useful context because the single statement is ambiguous.

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

Completeness1/5

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

With 9 optional parameters, no annotations, no output schema, and a complex set of sibling tools, the description is grossly inadequate. It does not explain what the tool returns, whether it performs validation, which parameters are relevant, or what 'discarded' means operationally. An agent cannot reliably select or invoke this tool based on the current description.

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

Parameters2/5

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

Although schema description coverage is 100%, the tool description does not map the parameters to its purpose. It only references a 'search query' without specifying which parameter that is (presumably 'query'), and it ignores the other 8 parameters. The description fails to explain how the parameters are used or why they exist, leaving the agent to guess the tool's actual behavior.

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

Purpose2/5

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

The description states a verb and resource ('Count characters in a search query'), but it is misleading because the schema lists 9 optional parameters, not just a search query. It does not explain what happens with the other parameters (ref, url, city, feed, host, json, path, zone) or how they relate to the stated purpose. The phrase 'Query discarded' is contradictory and unclear.

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?

There is no guidance on when to use this tool versus the 28 sibling tools, many of which are shape-validators (e.g., domain-shape, github-repo-shape). The description does not mention any conditions, alternatives, or exclusions.

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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