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ankitkapur1992-hlido

hlido-mcp

by ankitkapur1992-hlido
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Hybrid

submit_agent

Submit a new AI agent for Hlido to review when it's not yet in the corpus. The tool queues the review and returns a tracking reference.

Instructions

Nominate a new AI agent for Hlido to review. Use this when an agent isn't in Hlido's corpus yet (trust_check returned no_review_found) and you want it added. Returns a confirmation with a tracking reference; the review is queued and produces a public scorecard. If you need a verdict right now rather than a queued review, use request_quick_audit (faster, rate-limited) instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesThe agent's product or homepage URL (e.g. 'https://example.com').
nameYesHuman-readable agent name (e.g. 'Example Coder').
noteNoOptional context: what the agent does, or why it's worth reviewing.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv0.1.1
    • addedInput schema / properties / name / description
      Added value: +"Human-readable agent name (e.g. 'Example Coder')."
    • addedInput schema / properties / note / description
      Added value: +"Optional context: what the agent does, or why it's worth reviewing."
    • addedInput schema / properties / url / description
      Added value: +"The agent's product or homepage URL (e.g. 'https://example.com')."
  2. First observedv0.1.0

TDQS

A4.5/5.0
Behavior4/5

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

Describes outcome (confirmation with tracking, queued review, public scorecard). No annotations exist, so description carries full burden; it's transparent but doesn't mention potential queue times or rate limits.

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?

Two efficient sentences. First sentence covers purpose and condition; second provides alternative. No fluff.

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 3 params and no output schema, the description adequately covers usage context, result, and alternative. Complete for agent decision-making.

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

Parameters3/5

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

Schema coverage is 100%, so baseline is 3. The description adds minimal extra meaning beyond the schema's parameter descriptions; it doesn't elaborate on usage or syntax.

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's purpose: 'Nominate a new AI agent for Hlido to review.' It specifies the exact scenario (when agent not in corpus) and distinguishes from sibling request_quick_audit.

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

Usage Guidelines5/5

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

Explicitly tells when to use this tool: after trust_check returns no_review_found, and contrasts with request_quick_audit for immediate verdicts. Clear and actionable.

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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