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verIdyia

AutoEQ MCP Server

by verIdyia

eq_ranking

Read-onlyIdempotent

Compare headphones using Harman preference scores to identify models that match listener preferences for sound quality.

Instructions

Get headphone rankings by Harman headphone listener preference score.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
form_factorNoType: over-ear, in-earover-ear
limitNoNumber of entries

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The handler function `eq_ranking` queries the `headphones` table to retrieve and format headphone ranking data based on Harman preference scores.
    async def eq_ranking(
        form_factor: str = Field(default="over-ear", description="Type: over-ear, in-ear"),
        limit: int = Field(default=20, description="Number of entries"),
    ) -> str:
        """Get headphone rankings by Harman headphone listener preference score."""
        conn = get_db()
        rows = conn.execute(
            """SELECT name, source, form_factor, signature, score, std_db, slope
               FROM headphones
               WHERE score IS NOT NULL AND form_factor LIKE ?
               ORDER BY score DESC
               LIMIT ?""",
            (f"%{form_factor}%", min(limit, 50)),
        ).fetchall()
        conn.close()
    
        if not rows:
            return "No ranking data available."
    
        lines = [f"## Harman preference ranking ({form_factor}, top {len(rows)})"]
        lines.append(f"{'Rank':>4}  {'Score':>5}  {'STD':>5}  {'Slope':>6}  Model")
        lines.append(f"{'─'*4}  {'─'*5}  {'─'*5}  {'─'*6}  {'─'*30}")
        for i, r in enumerate(rows, 1):
            slope_mark = "↓" if (r["slope"] or 0) < -0.1 else ("↑" if (r["slope"] or 0) > 0.1 else "→")
            sig = f" ({r['signature']})" if r["signature"] else ""
            lines.append(
                f"{i:>4}  {r['score']:>5.0f}  {r['std_db']:>5.2f}  {r['slope']:>+6.2f}{slope_mark}  {r['name']}{sig}"
            )
        return "\n".join(lines)
  • autoeq_mcp.py:845-854 (registration)
    Registration of the `eq_ranking` tool within the MCP server.
    @mcp_server.tool(
        name="eq_ranking",
        annotations={
            "title": "Harman preference ranking",
            "readOnlyHint": True,
            "destructiveHint": False,
            "idempotentHint": True,
            "openWorldHint": False,
        },
    )
Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=false, covering safety and idempotency. The description adds no additional behavioral context (e.g., rate limits, authentication needs, or what 'ranking' entails beyond the score). It doesn't contradict annotations, but adds minimal value beyond them.

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 a single, efficient sentence with zero wasted words. It front-loads the core purpose ('Get headphone rankings') and specifies the ranking method, making it easy to parse and understand quickly.

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?

Given the tool's low complexity (2 optional parameters), high schema coverage (100%), presence of annotations, and output schema, the description is reasonably complete. However, it lacks guidance on usage relative to siblings, which is a minor gap in context for an agent navigating multiple tools.

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 description coverage is 100%, with clear descriptions for both parameters (form factor and limit). The description adds no parameter-specific semantics beyond what the schema provides, such as explaining the ranking criteria or default behaviors. Baseline 3 is appropriate given the schema's completeness.

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 specific action ('Get') and resource ('headphone rankings') with precise criteria ('by Harman headphone listener preference score'). It distinguishes this tool from siblings like eq_search or eq_recommend by focusing on ranking rather than searching or recommending.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives like eq_search or eq_recommend. It lacks any mention of prerequisites, exclusions, or comparative context with sibling tools, leaving the agent to infer usage scenarios.

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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