gapup-mcp

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

Name	Required	Description
`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.
`scope_type`	Yes
`tech_stack`	Yes
`asset_count`	No
`target_geos`	No
`engagement_type`	No
`retest_included`	No
`business_context`	Yes
`compliance_frameworks`	No

TDQS

B3.3/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds that it returns a structured, audited deliverable, but does not disclose additional behaviors such as authentication requirements, rate limits, or what happens on invalid inputs. 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.

Conciseness3/5

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

The description is front-loaded with the tool's core purpose, but it contains several example questions and a reference case, making it moderately verbose. It could be shortened while retaining key information.

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?

Given the tool's complexity (9 parameters, no output schema, many siblings), the description provides a good overview of the tool's function and example uses. However, it lacks details on the return format and expected behavior, which leaves some gaps for an AI agent.

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%. The description adds value by referencing key parameters (scope_type, tech_stack, asset_count, etc.) in example questions, helping users understand the tool's inputs. However, it does not describe each parameter systematically or cover all nine parameters.

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 estimates pentest scope, providing effort/cost estimates, engagement plans, and recommendations. It is specific to a niche tool, but does not explicitly differentiate from sibling security tools like cyber_risk_auditor or attack_surface_monitor.

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?

Example questions and a reference case imply when to use it, but there is no explicit guidance on when not to use it or mention of alternative tools. The description says 'Answers: ...' which sets clear expectations, but lacks exclusions or context for 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

C2.5/5.0

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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