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memory-key-count

Count keys in a JSON object. Values 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

C2.7/5.0
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It does reveal that values are discarded, which is useful, but it does not mention return format, whether nested keys are counted, invalid JSON behavior, side effects, or any safety-relevant traits.

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 two sentences are front-loaded, short, and contain no filler. The description is concise, but for a tool with nine optional parameters and no output schema, the brevity edges toward under-specification rather than efficient completeness.

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 definition does not give an agent enough information to invoke it correctly: it fails to say which parameter supplies the JSON, whether the count includes nested keys, what the returned value looks like, or how it relates to sibling tools. With no annotations and no output schema, this is a significant gap.

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?

Schema coverage is 100%, so a baseline of 3 would normally apply, but the description adds no explicit mapping to a parameter. Worse, several schema descriptions point to unrelated operations such as URL normalization, weather hints, and validation, so the schema is actively misleading about which parameter matters for counting keys.

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 names a specific operation ('Count keys') and a specific resource ('in a JSON object'), and 'Values discarded' adds a clear scope. However, it does not name which input parameter holds the JSON or explicitly distinguish itself from JSON-related siblings like validate-json, so it stops short of a 5.

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?

There is no guidance about when to use this tool versus alternatives such as validate-json or search-query-len. The only implied usage is 'when you need to count keys,' but no explicit context, exclusions, or alternative-selection hints are provided.

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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