packet-chef-mcp

packet_extract_conversations

Extract and rank bidirectional 5-tuple network conversations from PCAP captures, with sent/received packet and byte counts, duration, and TCP flags.

Instructions

Aggregates and tracks bidirectional 5-tuple conversations (src/dst IP, ports, protocol). Computes sent/received packet and byte counts, duration, and TCP flag lifecycles (SYN/ACK/FIN/RST). Ranked by total bytes transferred.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "packet_extract_conversations"
  }
}

Input Schema

Name	Required	Description
`input`	No	Base64-encoded PCAP file bytes (max 10MB).
`limit`	No	Maximum number of conversations to return (default: 50, max: 200).
`filePath`	No	Absolute path to a PCAP file on disk (local stdio only).

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.0.1Oct 1, 2026

TDQS

A3.8/5.0

Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It usefully discloses output structure (sent/received packet and byte counts, duration, TCP flag lifecycles) and the ranking rule (total bytes transferred), which is real behavior beyond the schema. However it says nothing about the input vs filePath trade-off, resource/latency behavior, or error handling.

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?

Four sentences, zero waste, front-loaded with the core identity before the computed metrics and the ranking clause. Every sentence contributes information.

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?

There is no output schema, so the description correctly compensates by describing what is returned (counts, duration, flag lifecycles, ranking). The remaining gap is operational: no guidance on choosing between the base64 'input' and on-disk 'filePath' modes, and no note on cost limits beyond the schema's stated 10MB.

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 100%, so the schema already documents input, limit, and filePath fully. The description adds no parameter-level meaning (e.g., which input mode is preferred, interaction between limit and ranking). Baseline 3 applies when the schema does the heavy lifting.

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 states a specific verb and resource ('Aggregates and tracks bidirectional 5-tuple conversations') and then enumerates exactly what is computed. The flow/conversation framing cleanly distinguishes it from the protocol-specific siblings (packet_extract_dns, packet_extract_http, packet_extract_tls_sni, etc.).

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?

No explicit when-to-use or when-not-to-use guidance, and no sibling is named as an alternative. Usage is only implied by the description: use it for flow-level, conversation-oriented analysis rather than payload extraction. An agent gets the gist but no routing rules.

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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