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mcp-knowledge

by io.github.getgapup

pentest_scope_estimator

Read-only

Estimateur de scope pentest — Gapup agent-payable C-suite expertise (RISK). Returns a structured, audited deliverable. Answers: For a pentest on with assets, what is the effort and cost estimate? · How much should I budget for a web application + API penetration test for SOC 2 Type II compliance? · What is the standard engagement plan (PTES phases + deliverables) for a pentest? · Which engagement type (black-box/grey-box/white-box/red-team) is recommended for my context? · What are the prerequisites and risks for a pentest engagement on my cloud infrastructure? Reference case: Acme SaaS Inc — Fintech B2B EU · web-app + API REST · 12 microservices Node.js AWS · . 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.
scope_typeYes
tech_stackYes
asset_countNo
target_geosNo
engagement_typeNo
retest_includedNo
business_contextYes
compliance_frameworksNo

TDQS

C2.7/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true. Description adds that inputs are validated server-side and returns a structured deliverable, but does not detail auth requirements, rate limits, or error behavior.

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, containing multiple questions and marketing phrases. It could be condensed to a few sentences without losing essential meaning.

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 9 parameters, no output schema, and low schema coverage, the description is insufficient. It does not explain return values, behavior for different parameter combinations, or prerequisites, leaving the agent under-informed for correct invocation.

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 11%, with most parameters lacking descriptions. The description lists example questions referencing some parameters (scope_type, tech_stack) but does not explain individual parameter semantics or how to use them correctly.

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 estimates pentest effort and cost based on scope, with example questions. However, it includes excessive marketing language ('Gapup agent-payable C-suite expertise (RISK)') that detracts from clarity. The purpose is distinct from siblings but could be more direct.

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, nor when not to use it. The description provides example queries but no explicit criteria for selection among many sibling 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

C2.8/5.0
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.

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