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KHOTEM — Cryptographic Witness

khotem_spa

PAID $0.02. Agent context SPA: dump noisy context/logs (text, ≤256KB) → distilled essence (rarity×diversity, mechanical, deterministic, corpus-calibrated F1=1.0), repeated patterns, compression ratio, sha256. Pay first, then pass payment_tx + text.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes
payment_txNo
max_essenceNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral disclosure load. It reveals the tool is paid ($0.02), deterministic, has an input size limit (≤256KB), and lists output features (essence, repeated patterns, compression ratio, sha256). It also states the payment flow (pay first, then pass payment_tx). This is substantial, though it does not mention side effects, error handling, or what happens if the payment fails.

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 highly compact, using a concise arrow notation and front-loading the paid status and purpose. Every sentence adds value: cost, operation, parameters, and payment flow. There is zero fluff and it fits a single breath, making it easy for an agent to parse quickly.

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?

Despite the complexity, the description covers the core operation, input limit, output features, and payment requirement. It is mostly complete for an agent to invoke correctly. However, the 'max_essence' parameter is left ambiguous, and there is no explicit output schema (though the description lists output elements). For a paid tool with no annotations, this is a solid attempt but leaves a minor gap.

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%, so the description must explain parameters. It clarifies 'text' as noisy context/logs and 'payment_tx' as a payment transaction token (implied via 'then pass payment_tx + text'). However, it does not explain 'max_essence' at all. This leaves one of three parameters undocumented, so the description only partially compensates for the missing schema descriptions.

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 clearly states the operation: it takes noisy context/logs (text) and produces a distilled essence with specific characteristics (rarity×diversity, deterministic, etc.). The verb-resource mapping is specific and the purpose is understandable. However, it does not explicitly differentiate this tool from the many khotem_* siblings, relying on the abbreviation 'SPA' which may be ambiguous.

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 description implies a use case ('Agent context SPA' and 'dump noisy context/logs') and a prerequisite ('Pay first'), but it does not explicitly state when to use this tool versus alternatives or provide any exclusion criteria. It gives context for usage but no direct comparison with sibling tools, so an agent must infer when this is appropriate.

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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