gapup-mcp

positioning_strategist

Read-only

Stratège de positionnement — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Reference case: Gapup Hub vs Tableau/Pigment/Looker — Angle de différenciation + 5 piliers messaging + battle plan. Inputs are validated server-side — send the documented case fields.

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.
`market`	Yes
`company`	Yes
`product`	Yes
`aspirations`	No
`competitors`	Yes
`customerPains`	Yes
`currentWeaknesses`	No

TDQS

C2.9/5.0

Behavior3/5

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

Annotations include readOnlyHint=true and openWorldHint=true, which are consistent with the description stating it returns a deliverable. The description adds that it is 'audited' but does not elaborate on latency, data usage, or other behavioral traits. With annotations present, the bar is lower; the description adds modest value.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is somewhat concise but includes a lengthy reference case and French phrases that may confuse non-native speakers. It is front-loaded but not optimally structured for quick scanning.

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?

The description provides a high-level view of the output (differentiation angle, 5 pillars, battle plan) but lacks details on output structure, async handling, and how it differs from similar sibling tools. With no output schema, more context would be beneficial.

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 at 13%, yet the description does not elaborate on any parameter meaning. It only instructs to 'send the documented case fields,' failing to compensate for the schema's lack of descriptions. The async parameter is ignored.

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?

The description clearly states the tool returns a structured, audited deliverable for positioning strategy, including differentiation angle, 5 pillars messaging, and battle plan. It references a specific case (Gapup Hub vs Tableau/Pigment/Looker). However, it does not explicitly distinguish from siblings like brand_builder or competitive_deep_dive.

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?

No guidance on when to use this tool versus alternatives. The description only provides an input validation note ('Inputs are validated server-side') and mentions a reference case, but not usage context 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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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.

Resources

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