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mcp-knowledge

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

C2.7/5.0
Behavior3/5

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

Annotations already declare readOnlyHint and openWorldHint. The description adds context about the output being a structured deliverable and mentions async execution, but does not contradict annotations. Adequate but not rich.

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 verbose, contains promotional language ('Gapup agent-payable C-suite expertise (CMO)'), and is not well-structured. It mixes questions, a reference case, and parameter info without clear organization.

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?

No output schema is provided, and the description's explanation of the return value ('structured, audited deliverable') is vague. It lacks details on output format, interpretation, or potential errors, making it incomplete for a tool with 4 parameters.

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 only 25%, with only the async parameter described. The tool description does not compensate; it does not explain the meaning or usage of depth, focus, or target_domain beyond what the schema provides.

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's function: analyzing the tech stack of a domain and answering multiple specific questions. It distinguishes itself from siblings by focusing on tech fingerprinting, but could be more concise.

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?

The description implies use cases through the questions it answers, but does not provide explicit guidance on when to use this tool versus alternatives, nor does it mention when not to use it.

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

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

Resources