Build a bounded multi-source ACE Trust Graph from authorized provider-qualified candidate IDs. Evidence may cross providers only through an explicit strong product-identity bridge and never for provider-bound offer/policy claims.
| Name | Required | Description | Default |
|---|---|---|---|
| claims | No | ||
| country | No | US | |
| candidate_ids | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses meaningful behavioral constraints: the graph is bounded, evidence can cross providers only through a strong product-identity bridge, and provider-bound offer/policy claims are excluded. With no annotations provided, this is helpful, but it still omits whether the operation has side effects, what the returned graph contains, or how failures are handled.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single dense sentence with the primary action front-loaded. Each clause adds a relevant constraint without excessive filler. It is concise, though slightly jargon-heavy and could benefit from a brief parameter mapping.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
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Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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. It maps 'candidate IDs' to the candidate_ids parameter and adds the 'authorized provider-qualified' qualifier, and 'evidence' likely maps to claims. However, the claims object and country parameter receive no meaningful explanation, leaving much of the parameter semantics ambiguous.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action ('Build') and resource ('bounded multi-source ACE Trust Graph'), and it identifies the input source ('authorized provider-qualified candidate IDs'). The constraints about cross-provider evidence help distinguish it from likely single-source siblings such as ace_check, though the acronym 'ACE Trust Graph' is not explained.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when the tool is appropriate: when building a multi-source trust graph from provider-qualified candidate IDs. It also includes an exclusion for provider-bound offer/policy claims. However, it never explicitly names sibling alternatives or gives clear when-to-use versus when-to-use-another-tool guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools target distinct workflow stages (discovery, identity, claims, decision, referral), and the detailed descriptions help separate them. However, ace_check and commerce_preflight both expose ACE Verdict decisions, and verify_global_offer is an explicitly legacy alternative to commerce_preflight, so an agent can still misselect among the verification/decision tools.
All names are readable snake_case, and action tools use a verb_object pattern while query/metadata tools use noun phrases. The split is not chaotic, but it is inconsistent (e.g., product_discovery vs resolve_product_identity), and the commerce_/commercial_ prefix variation adds a minor wrinkle.
Fifteen tools is at the upper end of a well-scoped service and is reasonable for a broad commerce intelligence domain. The count feels slightly heavy because there are several descriptive/policy tools and at least one legacy overlap (verify_global_offer) that could be consolidated.
The set covers the core pre-purchase workflow: discovery, identity resolution, claim compilation, verification, recalls, merchant readiness, trust, referrals, and routing. Minor gaps exist, such as no explicit claim-management or decision-status retrieval separate from the decision products, but these do not create dead ends.