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trust_stack_info

Retrieves details of all 7 Agent Trust Stack protocols, including purpose, whitepaper links, and implementation status for AI agent trust and coordination.

Instructions

Get information about all 7 Agent Trust Stack protocols.

Returns details for each protocol including name, purpose, whitepaper link,
PyPI package name, and implementation status. The Agent Trust Stack provides
a complete infrastructure layer for autonomous AI agent trust, accountability,
and coordination.

Returns:
    JSON with protocol list, overview, and installation instructions

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description carries the full burden. It clearly states the tool returns information and lists the return fields. It does not mention side effects (likely none), and the name suggests read-only behavior. The description is transparent about what the tool does.

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 extremely concise, front-loading the main purpose in the first sentence, then providing bullet-point-like details. Every sentence adds value with no wasted words.

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

Completeness5/5

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

Given zero parameters and the presence of an output schema, the description is complete. It clearly explains what the tool does and what the return value includes, leaving no ambiguity for a simple informational tool.

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

Parameters4/5

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

The tool has zero parameters, so baseline is 4. The description adds value by explaining what the output contains (protocol list, overview, installation instructions), which helps the agent understand what to expect.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool gets information about all 7 Agent Trust Stack protocols, listing specific details returned (name, purpose, whitepaper link, PyPI package, implementation status). This is specific and distinguishes it from siblings like get_trust_evidence or verify_agent_identity.

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

Usage Guidelines3/5

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

The description implies usage for getting an overview of the trust stack protocols, but does not explicitly state when to use this tool versus alternatives like get_trust_evidence or verify_agent_identity. No exclusions or when-not-to-use guidance is 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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