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America/Santarem clock

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

C2.9/5.0
Behavior2/5

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

With no annotations, the description carries the full burden. It discloses that the query is discarded, which is a useful behavioral trait, but it says nothing about how the count is computed (e.g., Unicode code points vs bytes), whether other parameters are ignored or processed, or what the return value looks like. The many parameters in the schema without explanation create ambiguity about the tool's actual behavior.

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

Conciseness5/5

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

The description is two short sentences with no wasted words. It front-loads the core action and then adds the disposal behavior. This is appropriately concise for a simple tool, though it may be too sparse given the complex schema.

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?

The tool has 9 parameters, no annotations, and no output schema, so the description must compensate. It fails to explain why the other parameters exist, how they relate to the 'search query' focus, or what the tool returns. The description only addresses the 'query' parameter, leaving the rest unexplained. For a tool with this much surface area, the description is incomplete.

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%, so the baseline is 3. The parameter descriptions in the schema, such as 'query' ('Search text; discarded after the length check'), already explain the relevant semantics. The tool description adds nothing beyond the schema for parameters. It does not clarify the role of the other 8 parameters, but since the schema covers them individually, the description does not need to repeat that information.

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

Purpose4/5

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

The description clearly states a specific action: 'Count characters in a search query.' It identifies the verb (count) and the resource (search query), and mentions that the query is discarded. However, it does not differentiate from sibling count tools like 'memory-key-count' or 'hn-front-count', and the presence of 9 unrelated parameters in the schema muddies the purpose. A clear statement of scope, but without sibling differentiation.

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

Usage Guidelines2/5

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

The description gives no guidance on when to use this tool versus alternatives. It does not mention any conditions or exclusions, nor does it point to other tools that might be more appropriate for different tasks. The agent is left to infer usage from the name alone, which is insufficient given the diverse sibling set.

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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