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OMENZ agent tools

context_diet

Context Diet: shrink a packet before it fills your context: drops empty fields, dedupes lists, cuts long strings to a head + sha256 pointer, caps list length, always keeps required fields; reports bytes and approximate tokens saved. By OMENZ. (MCP calls run in AYNE, the free playzone; production: POST https://www.omenz.org/api/tollbooth/v1/context-diet, $0.001 via x402.)

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "packet": "<packet>"
    },
    "name": "context_diet"
  }
}

Input Schema

NameRequiredDescriptionDefault
packetYes
requiredNo
max_list_itemsNo
max_string_charsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

No annotations are supplied, so the description must carry the full burden; it does so well by naming the exact transformations applied: dropping empty fields, deduping, capping, truncating with a sha256 pointer, and preserving required fields. What it leaves out is whether the original packet is mutated in place or left unchanged, and what happens on invalid input; those are material for a lossy transform tool.

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 tool behavior comes first and is packed into one dense clause that front-loads the highest-value information. The trailing attribution and paid production endpoint with pricing is promotional and operational rather than selection-relevant; it slightly dilutes the signal.

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?

Because no output schema exists, the description must describe the return value; it does so by stating that the tool reports bytes and approximate tokens saved. The remaining gaps are edge-case behavior on malformed packets and the mutability of the input.

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 0% across 4 parameters, so the description must compensate. It implicitly ties 'required' to the always-keep rule, 'max_list_items' to list caps, and 'max_string_chars' to string cutting, but it gives no defaults, units, or bounds, and it never explains the shape of the 'packet' parameter itself.

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 gives a concrete verb and resource: it shrinks a packet by dropping empty fields, deduping lists, truncating long strings to a head plus sha256 pointer, and capping list length. That is far more specific than the name alone, though it never contrasts itself with plausibly overlapping siblings such as dedupe or evidence_bundle.

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 phrase 'shrink a packet before it fills your context' implies the usage condition: it is a pre-processing step, applied before a payload is passed to another tool. However, no explicit exclusions are stated, and no conditions say when not to use it or which sibling to choose instead.

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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