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gapup-mcp

by io.github.getgapup

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
NameRequiredDescriptionDefault
asyncNoIf 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.
depthYesstandard
focusYestech-buying
target_domainYes

TDQS

B3/5.0
Behavior3/5

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

Annotations already provide readOnlyHint and openWorldHint, lowering the bar. The description adds server-side validation and 'audited deliverable' as behavioral context, but doesn't detail output format, rate limits, or what 'audited' entails. No contradiction 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.

Conciseness2/5

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

The description is a long, run-on list of questions with a reference case awkwardly appended, including stray punctuation ('· .'). It could be significantly restructured and trimmed without losing meaning, making it poorly organized and moderately verbose.

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

Completeness2/5

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

With no output schema and low parameter coverage, the description must carry more weight, but it doesn't. It covers use cases but omits essential invocation details such as depth levels, focus options, and expected return structure, making it incomplete for a tool with 3 required parameters.

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

Parameters1/5

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

Schema description coverage is only 25% (only async is described). The description fails to explain target_domain, focus, or depth semantics. The vague phrase 'send the documented case fields' and the reference case do not clarify parameter usage at all.

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 it returns a structured, audited deliverable and answers a list of specific questions about a domain's tech stack, buying signals, and security risks. It distinguishes itself from siblings by covering both sales and security perspectives, though it lacks an explicit verb like 'analyze' and doesn't directly compare to alternative tools.

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 enumerates six explicit use cases (tech stack identification, buying signals, supply-chain risk, outreach angle, CISO-style fingerprint, vendor adoption) which provide clear context for when to use the tool. However, it doesn't state when not to use it 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

C2.4/5.0
Disambiguation1/5

Over 50 tools share the identical template 'Gapup agent-payable C-suite expertise' with similar French descriptions and reference cases, making their boundaries indistinguishable. Clusters like competitor_intel, competitive_deep_dive, competitor_moves, competitor_profiles, competitor_pricing_radar, competitor_pricing_scrape, and competitor_recommendations heavily overlap in purpose.

Naming Consistency1/5

Names are chaotic: mix of French and English, snake_case and camelCase, verb_noun, noun, and adjective forms with no uniform pattern. Examples like 'bp_narratif', 'content_enrichment', 'ai_governance_full_report_async', and 'job_result' show no coherent naming convention.

Tool Count1/5

271 tools is far beyond any reasonable MCP server scope, creating an overwhelming selection burden for agents. This count vastly exceeds the 25+ threshold for 'too many' and makes navigation impractical.

Completeness2/5

While the server covers many business domains, it lacks lifecycle operations (e.g., no update/delete tools for the deliverables it generates) and the input specifications are vague ('documented case fields' without documentation), creating functional dead ends. The sheer breadth does not compensate for these gaps.

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