Trust verdict for a multi-agent team. Returns aggregate trust score (0-100), weakest-link analysis, and per-member breakdown. Teams with score >= 10 and 2+ members are considered trusted.
| Name | Required | Description | Default |
|---|---|---|---|
| team_id | Yes | Team ID to check trust for (e.g. team_abc123) |
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It transparently describes the output structure (aggregate score, weakest-link, per-member breakdown) and the trust threshold logic (score >= 10 and 2+ members). While it does not discuss potential failure modes or operational details, it gives a clear picture of what the tool returns and its decision rule, which is sufficient for a read-only check.
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 extremely concise and well-structured: the first sentence states purpose and output, the second sentence provides the trust threshold. Every sentence adds value, no wasted words or redundancy. It is front-loaded with the key information.
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?
For a simple read-only tool with one parameter and no output schema, the description covers the essential aspects: what it does, what it returns, and the trusted condition. It omits minor details like error behavior or interpretation of the weakest-link analysis, but these are not critical for an AI agent to select and invoke the tool correctly.
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?
The schema already provides 100% coverage for the single `team_id` parameter with a clear description and example. The tool description does not add further parameter semantics beyond what the schema offers. Baseline 3 is appropriate when the schema handles parameter documentation.
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 clearly states the tool's purpose: 'Trust verdict for a multi-agent team.' It specifies the output (aggregate trust score, weakest-link analysis, per-member breakdown) and even defines the trusted threshold. This distinguishes it from sibling tools like `trust_check` or `trust_compare` by focusing specifically on team-level trust.
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 provides clear context for when to use the tool (checking trust for a multi-agent team) and implies it is for team-level assessments. However, it does not explicitly mention alternatives or when not to use it, such as for individual trust checks via `trust_check` or comparisons via `trust_compare`. No exclusions are stated, but the context is enough for most use cases.
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.
Several tools overlap in purpose, such as bridge_erc8004_lookup and bridge_erc8004_trust_check, trust_check, get_agent, and get_agent_reputation all returning trust or reputation data. Descriptions help clarify the distinctions, but the similarities could lead an agent to select the wrong tool.
Naming uses a mix of conventions: verb-noun (get_agent, search_agents), noun-first (compliance_report, dns_discovery), and prefix-based groupings (trust_*, bridge_*). While snake_case is consistent, the inconsistent verb/noun ordering and synonyms (lookup, check, verify) make it less predictable.
With 19 tools, the set is slightly heavy but still justified by the platform's broad feature set covering trust checks, reputation, teams, wishes, compliance, and credentials. Some tools could be merged (e.g., bridge_erc8004_lookup and bridge_erc8004_trust_check), but the count is not excessive for the apparent scope.
The tool surface focuses heavily on reading and checking trust data, but lacks obvious lifecycle operations such as updating or deleting agents, creating wishes, or managing endorsements. The presence of create_team without corresponding team management (update/delete) leaves a notable gap.
