Skip to main content
Glama

Trust Switchboard

trust_check

Pass/fail a URL against the live policy before recommending it to a human. Fail closed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes

TDQS

A4/5.0
Behavior4/5

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

With no annotations provided, the description carries the behavioral burden. It discloses a key trait: fail closed, meaning uncertain or failed checks should be treated as failures. It also notes the check is against the live policy, adding useful behavioral context beyond the schema.

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 only two short sentences, front-loaded with the core action and followed by a critical behavioral note. Every word earns its place, with no fluff or repetition.

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

Completeness4/5

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

This is a simple one-parameter tool with no output schema, and the description sufficiently explains the outcome ('pass/fail'), the timing ('before recommending'), and the failure behavior ('fail closed'). It does not detail return format or edge cases, but for this tool's complexity, the description is reasonably complete.

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 0%, so the description must compensate, but it only restates 'URL' without adding format constraints, scheme requirements, or other semantics. The property name is self-explanatory, and the description confirms the URL is the object of the check, which is minimally adequate for a single parameter.

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: pass/fail a URL against the live policy. It is not a tautology and identifies the resource (URL) and the evaluation context (live policy), though it does not explicitly differentiate itself from sibling tools.

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

Usage Guidelines4/5

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

The phrase 'before recommending it to a human' gives clear usage context. It does not name alternatives or exclusion conditions, but an agent can infer when this tool should be invoked in a recommendation workflow.

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.