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pentest_scope_estimator

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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

A3.9/5.0
Behavior4/5

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

Annotations declare readOnlyHint=true and openWorldHint=true. The description adds that it returns a structured, audited deliverable and validates inputs server-side, providing useful context beyond the annotations. There is no contradiction.

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 necessary, containing branding noise ('Gapup agent-payable C-suite expertise (RISK)') and a list of example questions that could be condensed. It front-loads the purpose but could be more tightly structured.

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?

With 9 parameters and no output schema, the description provides a reasonable overview and usage examples, but lacks details on the deliverable's structure, how to interpret estimates, and edge-case behavior. The validation note is helpful but does not fully compensate for the missing output schema and under-documented parameters.

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 description coverage is only 11%, so the description must compensate. It adds meaning via placeholders like <scope_type>, <tech_stack>, and <N> assets, plus a reference case (Acme SaaS, web-app, Node.js AWS) that maps to several parameters. However, it does not clarify all parameters; target_geos, retest_included, and async are not addressed.

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 identifies the tool as a pentest scope estimator, with specific examples of the questions it answers (effort/cost estimates, budget, engagement plan, engagement type, prerequisites/risks). It distinguishes itself from sibling tools by focusing on pentest scoping/estimation rather than vulnerability scanning or attack surface monitoring.

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 provides multiple concrete usage scenarios via example questions, such as budgeting for SOC 2 compliance and choosing engagement types. It gives clear context for when to use the tool but does not explicitly mention when not to use it or name alternatives.

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.