gapup-mcp

incident_response_evidence_collector

Read-onlyIdempotent

As a CTO, gather forensic evidence (logs, network flows, MITRE TTPs) from public breach reports and threat intelligence sources to support incident response post-mortems. Inputs include incident identifiers, date ranges, or MITRE technique IDs. Outputs structured evidence with attack patterns, indicators of compromise, and source references. — pass async:true REQUIRED to avoid x402 timeout.

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.
`date_range`	No
`incident_id`	Yes	Unique identifier for the incident (e.g., CVE, GitHub Advisory ID)
`mitre_technique_ids`	No	List of MITRE ATT&CK technique IDs (e.g., T1059)
`include_network_flows`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes
`sources`	No
`timeline`	No
`warnings`	No
`indicators`	No
`incident_id`	No
`network_flows`	No
`attack_patterns`	No

TDQS

A4.1/5.0

Behavior4/5

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

The description adds important behavioral context beyond annotations, specifically the requirement to pass async:true to avoid x402 timeout. The annotations already declare readOnlyHint, openWorldHint, and idempotentHint, so the description supplements these with operational constraints.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

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

The description is concise and well-structured, front-loading the purpose and then specifying inputs and outputs. The inclusion of 'As a CTO' is slightly unnecessary but does not detract much. The async note is placed at the end, effectively highlighting a critical usage requirement.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that an output schema exists and annotations are present, the description covers the essential aspects: purpose, input types, output structure, and a key behavioral note (async requirement). It does not mention error handling or pagination, but for a read-only evidence collector, it is reasonably complete.

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 coverage is 60%, and the description adds limited semantic value by mentioning input types (incident identifiers, date ranges, MITRE technique IDs) and the important async parameter. It does not explain all parameters in detail, so it does not significantly elevate the baseline.

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 specifies the tool's purpose: gathering forensic evidence from public breach reports and threat intelligence to support incident response post-mortems. It mentions specific evidence types (logs, network flows, MITRE TTPs) and outputs structured evidence, which distinguishes it from sibling tools that focus on other aspects like vulnerability scanning or compliance audits.

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 clear context for when to use the tool (incident response post-mortems) and the required inputs (incident identifiers, date ranges, or MITRE technique IDs). However, it does not explicitly state when not to use it or mention alternative tools among the many siblings.

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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