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buzzsearch

BuzzSearch MCP Server

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by buzzsearch

Get quotes

get_quotes

Retrieve verbatim customer quotes from BuzzSearch, filter by pain point, source, or intensity, and page results for ad copy and audience research.

Instructions

Verbatim customer quotes extracted by a search, filtered and paged. Free. Each quote carries a stable id, the source url, a dimension (emotional_painpoints, recurring_painpoints, desired_outcomes, failed_solutions, objections, customer_lingo), an engagement score and an emotional intensity (1 to 5). For ad copy, pull emotional_painpoints and customer_lingo sorted by intensity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sortNointensity
limitNo
offsetNo
sourcesNo
containsNoCase-insensitive substring filter on the quote text.
search_idYesThe search id returned by `search` or `list_searches`.
dimensionsNo
min_intensityNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.1

TDQS

A3.7/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral burden and does well: it discloses cost ('Free'), the returned payload shape (stable id, source url, dimension, engagement score, intensity 1-5), and that results are paged. It does not mention auth requirements or rate limits, which prevents a 5.

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?

Three tight sentences, front-loaded with what the tool returns before the practical filtering advice. No filler text, though the payload-field list is slightly dense.

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 no output schema, the description appropriately explains what each quote carries and mentions pagination, which is the essential missing information. Coverage of the non-dimension filter parameters is the only real gap for an 8-parameter tool.

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 only 25%, so the description must compensate. It helpfully enumerates the full dimension enum and the 1-5 intensity range, and implies the sort behavior, but says nothing about sources, contains, limit, offset, or min_intensity, leaving several parameters undocumented.

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?

States a specific verb+resource and scope: 'Verbatim customer quotes extracted by a search, filtered and paged.' An agent can distinguish this from get_comments and search by the quote resource. It stops short of explicitly naming which sibling to use instead, which keeps it from a 5.

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?

Provides one concrete usage scenario ('For ad copy, pull emotional_painpoints and customer_lingo sorted by intensity'), which is genuinely actionable. However, there is no general when-to-use guidance and no explicit contrast with get_comments or search, so the agent must infer the boundary.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.