gapup-mcp

brand_equity_voice_share_calculator

Read-onlyIdempotent

Calculates brand equity voice share for CMOs by analyzing mentions across 500K+ news articles and forums from Common Crawl and Wayback Machine. Inputs include brand name, competitors, and time range. Outputs voice share percentage, sentiment distribution, and top sources. Ideal for competitive benchmarking and brand visibility tracking. Pass async:true to avoid timeout.

Input Schema

TableJSON Schema

Name	Required	Description
`async`	No	If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
`brand`	Yes
`time_range`	Yes
`competitors`	No
`include_forums`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes
`sources`	No
`warnings`	No
`top_sources`	No
`total_mentions`	No
`brand_voice_share`	No
`sentiment_distribution`	No
`competitor_voice_shares`	No

TDQS

A4/5.0

Behavior4/5

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

Annotations already indicate read-only, open world, and idempotent behavior. The description adds context about data sources (Common Crawl, Wayback Machine) and potential timeouts, and outlines outputs (voice share percentage, sentiment distribution, top sources). No contradictions with annotations.

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 extremely concise: three short sentences that front-load the primary purpose and quickly cover key inputs, outputs, and a usage tip. Every sentence adds value without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 5 parameters (2 required), nested objects, and an output schema, the description provides a high-level overview but lacks detail on parameter semantics and behavior for optional parameters. The existence of an output schema helps, but the description does not fully equip an agent to correctly populate all inputs.

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

Parameters2/5

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

Schema description coverage is low (20%), so the description should compensate. While it mentions brand, competitors, time range, and async flag, it fails to explain the 'competitors' array, 'include_forums' boolean, or the structure of the 'time_range' nested object. This leaves significant gaps for an agent.

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 calculates brand equity voice share for CMOs by analyzing mentions across 500K+ news articles and forums. It specifies inputs, outputs, and data sources, distinguishing it from sibling tools like 'brand_builder' or 'content_engine'.

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 provides guidance on when to use the tool ('Ideal for competitive benchmarking and brand visibility tracking') and hints at timeout behavior ('Pass async:true to avoid timeout'). However, it does not explicitly state when not to use or mention alternative tools.

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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TDQS

C2.5/5.0

Disambiguation2/5

With 271 tools, many have overlapping purposes (e.g., multiple competitor intel tools, multiple financial modelers, multiple ESG auditors). Detailed descriptions help slightly, but the sheer volume creates confusion. Agents would struggle to select the right tool among many similar options.

Naming Consistency1/5

Tool names are wildly inconsistent: mix of English and French, snake_case and short phrases, some very generic (process, run, execute equivalents). No discernible naming convention (e.g., abm_architect vs. boundary_control vs. bp_narratif). This makes it hard to predict tool names.

Tool Count1/5

271 tools is far beyond typical well-scoped servers (3-15). This indicates an unfocused, over-bloated tool surface. Even for a general business intelligence server, this number is excessive and violates the principle of each tool earning its place.

Completeness2/5

Despite the large count, coverage feels scattered. Some domains (e.g., content, competitive intel) have many tools, while others (e.g., supply chain, HR) have gaps. The set lacks a coherent scope; it seems like a dump of many separate tool collections rather than a complete, curated surface.

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