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timps-swarm

timps-swarm

by Sandeeprdy1729
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

timps_observability_cost_optimizer

Analyze observability costs and recommend optimizations: cardinality pruning, sampling, log-volume cuts, and tier migrations with projected savings.

Instructions

Analyse your observability bill (Datadog / New Relic / Grafana Cloud / Splunk) and recommend cardinality pruning, sampling, log-volume cuts, and tier migrations with projected savings.

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_observability_cost_optimizer"
  }
}

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
Behavior1/5

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

With no annotations, the description carries full responsibility for disclosing behavioral traits. It does not mention whether the tool modifies anything, requires permissions, or has side effects. It only says it 'recommends', leaving transparency lacking.

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 concise sentence that gets straight to the point. It includes all essential information without any fluff or unnecessary details.

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 the tool's specific purpose and simple schema, the description is quite complete. It mentions the input (observability bill) and the output (recommendations with projected savings), covering the main aspects adequately.

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 both parameters, and the descriptions in the schema are generic. The tool description adds no additional context or constraints beyond the schema, so a baseline score of 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 the tool's function: analysing observability bills and recommending cost-saving actions with projected savings. It distinguishes itself from sibling tools by focusing on observability cost optimization.

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 provides no guidance on when to use this tool versus alternatives. It does not mention any specific scenarios or exclusions, leaving the usage context ambiguous.

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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