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timps-swarm

timps-swarm

by Sandeeprdy1729
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

timps_learning_agent

Analyse past successful runs to distil prompt improvements for all agents, transforming successes into actionable insights.

Instructions

Analyse past successful runs and distil prompt improvements for all agents.

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_learning_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

C2.9/5.0
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It suggests read-only analysis ('analyse', 'distil') but doesn't disclose side effects—does it write improvements back to disk, require history/state, or have side effects on other agents' configs? For a tool that improves 'all agents', the absence of side-effect and access information is a notable gap.

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?

A single, concise 11-word sentence that is front-loaded with the primary action ('Analyse past successful runs'). Every word earns its place; no filler or redundancy.

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 an ambiguous optional 'request' parameter, the description should clarify expected outputs, side effects, or typical invocation context. Given the enormous sibling list with many similar-sounding agents, the description should do more to disambiguate when this is the right agent.

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 100%, so the baseline is 3 per the rubric. The description adds no parameter detail, but the schema documents 'request' and 'language' adequately. The description could have explained why 'language' matters for a learning agent, but the schema does the heavy lifting here.

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 uses specific verbs and resources: 'Analyse past successful runs' and 'distil prompt improvements', clearly scoped to 'all agents'. It differentiates from siblings like timps_run_task and timps_self_critic_agent by focusing on learning/optimizing from past runs. However, it's slightly generic and could be mistaken for a meta-tool without more operational context.

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?

No guidance is given on when to use this tool versus the many overlapping siblings (timps_pattern_detector, timps_self_critic_agent, timps_run_task, timps_agent_composer). The learning intent is implied but there's no explicit when/when-not, no exclusions, and no mention of alternatives.

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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