gapup-mcp

domain_tech_fingerprint

Read-only

Empreinte tech d'un domaine — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Answers: What is the tech stack of — frontend, CMS, analytics, CRM, CDN, hosting? · What buying signals does 's technology footprint reveal for sales prospecting? · Analyze for supply-chain technology risk and third-party vendor exposure. · What is the best outreach angle for a sales rep targeting based on their detected stack? · Run a CISO-style technology fingerprint on — identify legacy tech, missing security headers, and vendor risk. · Has recently changed their marketing or analytics stack — any vendor adoption signals? Reference case: velora-payments.io · Next.js + Cloudflare + Stripe + GA4 + HubSpot · . Inputs are validated server-side — send the documented case fields.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`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.
`depth`	Yes		standard
`focus`	Yes		tech-buying
`target_domain`	Yes

TDQS

A3.8/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the description adds value by noting the deliverable is 'audited' and that inputs are validated server-side. It does not contradict annotations and provides context about the output's nature.

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 longer than needed, with a bulleted list of questions and a reference case. While front-loaded with purpose, it contains redundant details that could be trimmed. A more concise description would improve efficiency.

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 no output schema, the description adequately covers the tool's purpose and typical results through example questions. It mentions the 'async' parameter implicitly via the example reference, but does not explain its use. Overall, it is reasonably complete for a 4-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 low (25%), but the description partially compensates by linking the 'focus' enum values to the listed questions. However, 'depth' and 'target_domain' are not explained, and the mention of 'documented case fields' is vague. Some additional meaning is added, but not comprehensive.

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 returns a structured tech stack deliverable for a domain. It lists specific questions it answers (frontend, CMS, analytics, etc.), distinguishing it from sibling tools like competitive_deep_dive or competitor_intel, which focus on broader competitive analysis.

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 lists use cases (buying signals, supply-chain risk, outreach angles) but does not explicitly state when not to use this tool or compare to alternatives. Usage is implied but lacks clear boundaries.

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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