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mcp-knowledge

by io.github.getgapup

attack_surface_monitor

Read-only

Surveillance surface d'attaque — Gapup agent-payable C-suite expertise (RISK). Returns a structured, audited deliverable. Answers: Which Internet-facing assets of combine a critical CVE, an exposed service, and no WAF — top findings to fix in 14 days? · What is the attack surface of : subdomains, open ports, SSL/TLS grades, and associated CVEs? · Give me a CISO-ready ASM report with blast radius estimate and SLA-driven remediation plan for . · What is the email phishing risk for ? Assess SPF/DMARC posture and recommend improvements. · During M&A due diligence, what are the top cyber exposures on 's Internet-facing infrastructure? Reference case: Velora Payments — 8 assets exposés · 2 critiques (CVE-2023-44487 HTTP/2 RapidReset, Admin panel ouvert) · . 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.
focusNo
domainYes
exclusionsNo
scope_cidrsNo
include_email_surfaceYes

TDQS

B3.1/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint=true and openWorldHint=true. The description adds value by stating it returns a 'structured, audited deliverable' and that inputs are validated server-side. This context goes beyond the annotations without contradicting them.

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, mixing general purpose with multiple example questions. It lacks clear structure or prioritization of key information. Though front-loaded with the title, the text is a dense block that could be more concise.

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?

Given 6 parameters, low schema coverage, and no output schema, the description should be more complete. It covers the 'what' but omits details on parameters, return format, and limitations. For a complex tool, this is insufficient.

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 17%, so the description must compensate. It mentions domain and include_email_surface implicitly via examples, but does not explain focus, exclusions, scope_cidrs, or async. No parameter details or usage hints are provided.

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 identifies the tool as an attack surface monitor that returns a structured, audited deliverable. Example questions illustrate its functionality well. However, it does not explicitly differentiate it from sibling tools like cyber_risk_auditor or cve_security_lookup, which may overlap in purpose.

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?

The description provides example queries that imply usage for domain attack surface assessment, email phishing risk, M&A due diligence, etc. However, it lacks explicit guidance on when to use this tool versus alternatives, and does not state 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