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MolTrust MCP Server

mt_get_trust_score

Get the Swarm Intelligence Trust Score for an agent (Phase 2).

Score combines direct endorsements, propagated trust from endorsers,
cross-vertical credential bonus, and interaction proof activity.
Returns null/withheld if fewer than 3 independent endorsers (non-seed).
Seed agents get their base score directly.

Args:
    did: DID of the agent to score (e.g. "did:moltrust:a1b2c3d4e5f67890")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
didYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses the score composition, the null/withheld condition based on endorser count, and special handling for seed agents. This gives the agent useful behavioral context beyond a bare 'get'. It does not explicitly state that the operation is read-only, but the verb 'Get' and lack of mutation hints make that reasonable.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, with a brief two-sentence summary before the parameter documentation. It avoids fluff and stays focused. The structure is simple and front-loaded, though the parameter section could be more integrated rather than a separate 'Args' block. Still, it earns its keep.

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?

With an output schema present (though not shown), the description does not need to detail return fields. It covers the key conditions (null/withheld, seed handling) and the score composition, which are sufficient for calling the tool correctly. It leaves room for more detail about the output format, but the output schema presumably fills that gap.

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?

Schema coverage is 0%, so the description must compensate. It does provide a clear explanation of the 'did' parameter and an example value, which helps the agent form a valid DID. It could add format constraints or clarify that it is a string of a specific pattern, but the example covers the essential meaning.

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 the action (Get) and the specific resource (Swarm Intelligence Trust Score for an agent), and includes a phase indicator. It is distinguishable from sibling name variants by the unique 'Swarm Intelligence' qualifier, though it does not explicitly name alternatives. This is clear but not as sharply differentiating as the get_calls example.

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 explains what the tool does and conditions that affect the result (null/withheld if fewer than 3 independent endorsers), but it does not explicitly state when to use this tool versus other scoring/trust tools in the sibling list (e.g., moltrust_score, moltrust_reputation, mt_get_swarm_graph). Usage context is implied but not explicitly contrasted with alternatives.

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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