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MolTrust MCP Server

by io.github.MoltyCel
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mt_endorse_agent

Issue a W3C SkillEndorsementCredential for another agent.

Requires a valid evidence_hash from mt_create_interaction_proof
(max 72h old). Self-endorsement is rejected.
Contributes to the endorsed agent's Trust Score.

Args:
    endorser_api_key: MolTrust API key of the endorsing agent
    endorsed_did: DID of the agent to endorse
    skill: Skill being endorsed (python, javascript, security, prediction, trading, data_analysis, api_integration, smart_contracts, nlp, computer_vision, general)
    evidence_hash: SHA-256 hash from mt_create_interaction_proof (sha256:...)
    evidence_timestamp: ISO 8601 timestamp from mt_create_interaction_proof
    vertical: MolTrust vertical (skill, shopping, travel, prediction, salesguard, sports, core)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
skillYes
verticalYes
endorsed_didYes
evidence_hashYes
endorser_api_keyYes
evidence_timestampYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior4/5

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

With no annotations, the description must disclose behavior. It does so by noting that the action 'Contributes to the endorsed agent's Trust Score' and that self-endorsement is rejected, plus the freshness constraint on evidence. It stops short of describing error cases or whether the credential is publicly visible, but for a credential issuance tool these are the key side effects an agent needs to know.

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 well-organized: a one-sentence purpose, then two prerequisite/effect statements, then a clean bulleted list of arguments. It is concise yet complete, with no fluff or redundant phrasing. The most important info is front-loaded, and the args list is easy to scan.

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?

For a tool with six required parameters and zero schema documentation, the description covers all necessary operational details: purpose, prerequisites, param meanings, allowed enum values, and side effects. The output schema (which exists) presumably documents the return value, so the description does not need to repeat it. An agent can confidently invoke this tool after reading the description.

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

Parameters5/5

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

The schema provides zero descriptions for all six parameters (coverage 0%). The description compensates fully: it explains each parameter's meaning, gives the allowed values for 'skill' and 'vertical', and specifies the source and format for 'evidence_hash' (sha256:...) and 'evidence_timestamp' (ISO 8601). This is essential because an agent would otherwise have no way to know what to pass.

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 opens with a precise verb and object: 'Issue a W3C SkillEndorsementCredential for another agent.' This clearly distinguishes it from siblings like mt_skill_issue_vc (which likely issues a skill credential to self) and mt_create_interaction_proof (which is the prerequisite evidence generator). The tool's role is unambiguous.

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 description states a clear prerequisite ('Requires a valid evidence_hash from mt_create_interaction_proof (max 72h old)') and an exclusion ('Self-endorsement is rejected'), which tells an agent when it is valid to call. It implies the workflow (create proof first, then endorse) but does not explicitly name alternatives or say 'use this instead of X'. However, the context is strong enough for a competent agent to infer the intended usage.

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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