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labeveryday
by labeveryday

find_tcp_issues

Analyze TCP packet captures to detect retransmissions, resets, zero window, and duplicate ACKs, identifying network problems with severity and recommendations.

Instructions

Analyze TCP packets for issues like retransmissions and resets.

Use this tool to diagnose network problems. Detects:

  • Retransmissions (indicating packet loss)

  • TCP resets (connection problems)

  • Zero window (buffer exhaustion)

  • Duplicate ACKs (packet loss signals)

Args: file_path: Path to the pcap or pcapng file max_packets: Maximum packets to analyze (default: 100000)

Returns: TCP issues categorized by type with severity and recommendations

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
file_pathYes
max_packetsNo
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses the types of issues detected (retransmissions, resets, zero window, duplicate ACKs) and implies this is a read-only analysis. It does not explicitly state non-destructiveness or resource implications, but the max_packets parameter hints at performance considerations.

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?

The description is very concise with no fluff. It opens with a one-sentence purpose, then a usage line, a bullet list of detections, and finally Args/Returns. Every sentence earns its place.

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?

The tool has no output schema, so the description must explain return values. It states 'TCP issues categorized by type with severity and recommendations,' which is sufficient for a diagnostic tool. It could be more specific about the structure, but it provides enough context for an AI agent to understand what to expect.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% description coverage, but the tool description includes an explicit 'Args' section explaining each parameter: file_path is a pcap/pcapng file, max_packets has a default of 100000. This compensates fully, adding clear meaning beyond the schema's type and title.

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 states the tool analyzes TCP packets for specific issues like retransmissions and resets. It lists four distinct types of TCP problems, distinguishing it from sibling tools that focus on other protocols (e.g., analyze_dns_traffic) or network metrics.

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 explicitly says 'Use this tool to diagnose network problems,' which provides clear context. It does not mention when not to use it or suggest alternatives, but given the tool's focused purpose, the guidance is adequate.

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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