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pclas_info

[FREE] PCLAS - Predictive Cognitive-Load Anticipation System for agent fleets. Forecasts each agent's workload over a horizon (Holt exponential smoothing on provided history, or deterministic synthesis), detects overload peaks BEFORE they happen, and computes a proactive task reassignment plan moving low-priority heavy tasks to agents with headroom. Verdicts: STABLE (no overload), REASSIGN (plan clears all peaks), CRITICAL (overload survives reassignment). This tool returns service info and terms. The paid forecast endpoint is https://agentpaystore.com/pclas/api/forecast (POST, $0.05 USDC via x402 on Base L2, HTTP 402 without X-Payment). Input: agents 1-50 (id, capacity, load, optional history 1-24 points), optional tasks 0-200 (id, agent_id, weight, priority 1-5), horizon 1-24, volatility 0-1.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

B3.1/5.0
Behavior3/5

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

With no annotations, the description carries the burden. It discloses cost mechanics for the paid endpoint ($0.05 USDC, x402 on Base L2, HTTP 402 without X-Payment) and labels this tool [FREE], which is useful. It says nothing about what this info tool actually returns, its auth needs, or rate limits.

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

Conciseness2/5

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

A single dense block that front-loads a marketing-style capabilities pitch while the one sentence describing this tool's function sits in the middle. Most of the text serves the paid endpoint rather than the info tool, so the structure does not front-load what matters.

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?

An output schema exists, so return values need not be spelled out, and a zero-parameter info tool is simple. Still, the description never characterizes what the 'service info and terms' contain, leaving the description top-heavy on an unrelated endpoint.

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

Parameters4/5

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

The tool takes zero parameters and the schema is empty, so the baseline is 4. The parameter details in the text (agents 1-50, tasks 0-200, horizon, volatility) describe the paid forecast endpoint's input, not this tool's, so they add no semantic value here.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The actual purpose — 'This tool returns service info and terms' — is stated, but it is buried mid-paragraph after a long pitch for the forecast service that this tool does not perform. An agent skimming the opening could wrongly conclude pclas_info runs a forecast, and nothing distinguishes it from the many sibling *_info tools.

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/when-not guidance, but the text points to the paid forecast endpoint (URL, POST, price) as where the real forecasting work happens, which implicitly routes the agent. The link between 'call this info tool first, then the paid endpoint' is left for the agent to infer.

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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