Skip to main content
Glama

Trust Switchboard

nutrition_label

Stamp of what it is + last test.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes

TDQS

C2/5.0
Behavior2/5

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 disclosing behavioral traits. It does not state whether the tool is read-only, whether it has side effects, whether it requires authorization, what happens on failure, or what the output actually contains. 'Stamp' hints at a summary-like result but is not explicit.

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 short, but brevity is not clarity. 'Stamp of what it is + last test' is cryptic and under-specified; it does not front-load useful operational information. It reads more like an incomplete note than a tool definition.

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?

Even though the tool has only one parameter and no output schema, the description is far from complete. It fails to explain what the nutrition label represents, what role the URL plays, what the 'last test' refers to, or what the returned result looks like. An agent has too little context to invoke the tool confidently.

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

Parameters1/5

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

Schema description coverage is 0%, and the description never mentions the required 'url' parameter or explains what URL should be supplied or how it is used. The description provides no semantic value beyond the raw schema, so an agent cannot infer the expected input format or meaning.

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 'Stamp of what it is + last test' is evocative but vague: it suggests the tool produces some kind of label or summary, and the tool name 'nutrition_label' helps slightly. However, it lacks a clear verb, resource, or explicit statement of what action is performed, and it does not distinguish the tool from its siblings.

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 provides no guidance on when to use this tool versus alternative tools. There is no mention of context, prerequisites, or exclusions, and the sibling tool names do not help clarify selection criteria.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

C2.9/5.0
Disambiguation2/5

Several tools cluster around pre-recommendation and human approval, so boundaries are blurry: commit_recommend, log_intent, whats_good_for, and trust_check all happen 'before recommending,' while request_approve and decide_approve differ mainly by who initiates. Descriptions help, but an agent could easily pick the wrong tool.

Naming Consistency4/5

Most tools follow an imperative verb_noun snake_case pattern—log_click, spend_check, trust_check, ingest_listing—making the set predictable. nutrition_label and whats_good_for break that pattern, but the overall style is still consistent enough to navigate.

Tool Count5/5

Ten tools fits the ideal 3-15 range and maps well to the server's trust-check, approval, logging, and listing-ingestion lifecycle. Each tool has a distinct role even if a few overlap conceptually.

Completeness4/5

The core workflow is well covered: policy checks, candidate lookup, logging, human approval, listing ingestion, and a nutrition stamp are all present. Missing observability and management endpoints like approval status/history or listing update/delete are workable gaps rather than dead ends.