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verIdyia

AutoEQ MCP Server

by verIdyia

eq_search

Read-onlyIdempotent

Search the AutoEQ database to find headphones and IEMs by name, type, sound signature, or measurement source for equalization settings.

Instructions

Search the AutoEQ database for headphones/IEMs. Filter by name, type, sound signature, or measurement source.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNoSearch term (model name, brand, etc.)
form_factorNoType filter: over-ear, in-ear, earbud
signatureNoSound signature filter: Neutral, Warm, Bright, Dark, V-shaped, U-shaped, Bass-heavy, Mid-forward, Harman-like
sourceNoMeasurement source filter: oratory1990, crinacle, Rtings, etc.
limitNoMax results (up to 50)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The `eq_search` function performs a database query against the `headphones` table to find items matching the provided query, form factor, signature, and source, returning a formatted markdown list.
    async def eq_search(
        query: str = Field(default="", description="Search term (model name, brand, etc.)"),
        form_factor: str = Field(
            default="",
            description="Type filter: over-ear, in-ear, earbud",
        ),
        signature: str = Field(
            default="",
            description="Sound signature filter: Neutral, Warm, Bright, Dark, V-shaped, U-shaped, Bass-heavy, Mid-forward, Harman-like",
        ),
        source: str = Field(
            default="",
            description="Measurement source filter: oratory1990, crinacle, Rtings, etc.",
        ),
        limit: int = Field(default=20, description="Max results (up to 50)"),
    ) -> str:
        """Search the AutoEQ database for headphones/IEMs. Filter by name, type, sound signature, or measurement source."""
        conn = get_db()
        conditions = []
        params = []
    
        if query:
            conditions.append("name LIKE ?")
            params.append(f"%{query}%")
        if form_factor:
            conditions.append("form_factor = ?")
            params.append(form_factor)
        if signature:
            conditions.append("signature LIKE ?")
            params.append(f"%{signature}%")
        if source:
            conditions.append("source LIKE ?")
            params.append(f"%{source}%")
    
        where = " AND ".join(conditions) if conditions else "1=1"
        sql = f"""
            SELECT name, source, coupler, form_factor, signature, score
            FROM headphones
            WHERE {where}
            ORDER BY score DESC NULLS LAST, name
            LIMIT ?
        """
        params.append(min(limit, 50))
        rows = conn.execute(sql, params).fetchall()
        conn.close()
    
        if not rows:
            return "No results found."
    
        lines = [f"## Search results ({len(rows)} found)"]
        for r in rows:
            score = f" [score:{r['score']}]" if r["score"] else ""
            sig = f" ({r['signature']})" if r["signature"] else ""
            coupler = f" [{r['coupler']}]" if r["coupler"] else ""
            lines.append(
                f"- **{r['name']}** — {r['source']}{coupler} | {r['form_factor']}{sig}{score}"
            )
        return "\n".join(lines)
Behavior4/5

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 useful context about the database scope (AutoEQ) and filterable attributes, though it doesn't mention rate limits, authentication needs, or pagination behavior beyond the limit parameter.

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 that front-loads the core purpose and immediately lists the filterable attributes without any wasted words or redundant information.

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 moderate complexity (5 parameters), rich annotations, 100% schema coverage, and the presence of an output schema, the description is reasonably complete. It covers the purpose and filter scope adequately, though it could benefit from more explicit usage guidelines relative to sibling 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 each parameter well-documented in the schema. The description adds minimal value beyond the schema by listing filter types (name, type, sound signature, measurement source), but doesn't provide additional syntax, format details, or constraints beyond what's already in the schema.

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 ('Search') and resource ('AutoEQ database for headphones/IEMs'), and distinguishes from siblings by specifying the search/filter functionality rather than comparison, profiling, ranking, recommendation, synchronization, or target-setting operations.

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 context by listing filterable attributes (name, type, sound signature, measurement source), but doesn't explicitly state when to use this tool versus alternatives like eq_compare or eq_recommend, nor does it mention any prerequisites or exclusions.

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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