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timps-swarm

timps-swarm

by Sandeeprdy1729
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

timps_agri_commodity_forecaster

Forecast Indian agri-commodity prices by analyzing mandi arrivals, MSP, weather, and festival demand. Get price direction, confidence, and a hedge recommendation for farmers, FPOs, and traders.

Instructions

Forecast Indian agri-commodity prices (mandi arrivals, MSP, weather, festival demand). Returns direction, confidence, and a hedge recommendation for farmers / FPOs / traders.

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": "timps_agri_commodity_forecaster"
  }
}

Input Schema

NameRequiredDescriptionDefault
requestNoPlain-English task or context for the agent.
languageNoPrimary programming language (default: python).python

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.2.1

TDQS

B3.2/5.0
Behavior2/5

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

No annotations are provided, so the description must fully disclose behavioral traits. It mentions that it returns direction, confidence, and a hedge recommendation, but omits any side effects, limitations, required inputs, or unusual behavior like the 'language' parameter being a programming language, which seems irrelevant to forecasting. The lack of detail leaves significant ambiguity.

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 a single, dense sentence that front-loads the core purpose and outputs. It has zero fluff and every word contributes to understanding the tool's function.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no output schema and vague parameters, the description should provide more detail on how to invoke it effectively. It lists factors and outputs but does not explain what 'request' should contain, how the language parameter affects results, or what format the outputs take. The open-ended request requires more guidance for correct agent usage.

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 coverage is 100% for the two parameters, but the descriptions are generic ('Plain-English task' and 'programming language') and do not explain how they relate to this specific tool. The description adds no extra meaning beyond what the schema already states, so baseline 3 is appropriate.

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 it forecasts Indian agri-commodity prices, listing specific inputs (mandi arrivals, MSP, weather, festival demand) and output (direction, confidence, hedge recommendation). This specifics verb+resource and distinguishes it from the generic timps_demand_forecaster sibling.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives context (target users) but provides no explicit guidance on when to use this tool vs alternatives like timps_demand_forecaster. It lacks any when-not-to-use conditions or references to sibling tools, leaving the agent to infer usage.

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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