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timps-swarm

timps-swarm

by Sandeeprdy1729
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

timps_fssai_compliance_agent

Assess F&B products against India's FSSAI regulations, covering license types, label requirements, additive and claim restrictions, and generate a compliance checklist.

Instructions

Assess an F&B product / packaging against India's FSSAI regulations: license type, label checklist, additive and claim restrictions, and a submission-ready checklist.

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_fssai_compliance_agent"
  }
}

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

A4/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It does disclose key behavioral outputs (license, label, additive, claim checks, checklist). However, it omits limitations, caveats, or information about how the agent handles ambiguous requests, making the transparency adequate but not outstanding.

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, well-structured sentence with a clear verb and enumerated scope. Every phrase earns its place, with no padding or repetition.

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?

Given no output schema and no annotations, the description does a good job of naming both assessment categories and the final deliverable. It is sufficiently detailed for an agent to select and tune the task, though it could be enhanced with how the request should be phrased or what optional details the agent may accept.

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% since both 'request' and 'language' have descriptions. The tool description adds no parameter-specific meaning beyond the schema, so the baseline of 3 is appropriate; the parameter semantics are adequately documented but not enhanced by the description.

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 identifies the tool's specific function: assessing F&B products/packaging against India's FSSAI regulations. It lists concrete deliverables (license type, label checklist, additive and claim restrictions, submission-ready checklist), which distinguishes it from the many sibling compliance tools like timps_gst_compliance or timps_dpdp_act_auditor.

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

Usage Guidelines4/5

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

The description implies a clear use case: when an F&B product/packaging needs FSSAI compliance assessment. It does not explicitly state when not to use the tool or name alternatives, which is a minor gap, but the domain-specific wording effectively guides the agent toward the right context.

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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