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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
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.
date_rangeNo
incident_idYesUnique identifier for the incident (e.g., CVE, GitHub Advisory ID)
mitre_technique_idsNoList of MITRE ATT&CK technique IDs (e.g., T1059)
include_network_flowsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
sourcesNo
timelineNo
warningsNo
indicatorsNo
incident_idNo
network_flowsNo
attack_patternsNo

TDQS

A3.9/5.0
Behavior4/5

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

Beyond readOnlyHint and idempotentHint annotations, description adds that it gathers from public sources and requires async:true to avoid timeout. Discloses output structure.

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

Conciseness5/5

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

Two sentences: first defines purpose and outputs, second emphasizes async requirement. No wasted words.

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?

Explains purpose, inputs, and async necessity, but fails to mention that async mode requires polling via job_result tool. Given output schema exists, this omission reduces completeness.

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?

Description lists key inputs (incident identifiers, date ranges, MITRE technique IDs) but does not add details beyond schema descriptions. Schema coverage is 60%, so baseline 3 is appropriate.

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?

Clearly states the tool gathers forensic evidence (logs, network flows, MITRE TTPs) from public breach reports and intelligence sources for incident response post-mortems. Distinguishes from siblings like ai_act_incident_response.

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?

Implies use during incident response post-mortems but provides no explicit guidance on when to use this tool vs alternatives or exclusions.

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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