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timps-swarm

timps-swarm

by Sandeeprdy1729
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

timps_research_agent

Searches documentation, GitHub, Stack Overflow, and the web to gather context before coding, returning a structured brief with findings, gotchas, recommended approach, and sources.

Instructions

Research Agent — runs BEFORE any coding task. Searches documentation, GitHub issues, Stack Overflow, and the web to gather context so all downstream agents have grounded information. Returns a structured brief: findings, gotchas, recommended approach, sources.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "topic": "<topic>"
    },
    "name": "timps_research_agent"
  }
}

Input Schema

NameRequiredDescriptionDefault
depthNo'quick'|'medium'|'deep'medium
topicYesWhat to research (e.g. 'FastAPI JWT auth best practices 2024')
sourcesNoOptional: specific sources to search (docs, github, stackoverflow)

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?

No annotations are provided, so the description bears the full burden. It does describe the sources searched and the return format (structured brief with findings, gotchas, approach, sources), but doesn't disclose potential side effects, permissions, rate limits, or error behavior. For a read-only research tool, this is adequate but not rich.

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?

Two concise sentences: the first states the purpose and timing, the second describes the return value. No wasted words and front-loaded information.

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 it has a simple input schema (3 params, all covered) and no output schema, the description adequately covers the return structure. It doesn't mention timing or async behavior, but for a research tool that returns a brief, it's sufficiently complete.

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%, meaning all parameters (topic, depth, sources) already have descriptions in the schema. The tool description adds no additional meaning beyond what's in the schema, so a baseline 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 identifies the tool as a research agent that runs before coding tasks, searches multiple sources (documentation, GitHub issues, Stack Overflow, web), and returns a structured brief. The verb 'research' and explicit resource scope distinguish it from other agents, though it shares similarities with deep_research_agent but here the differentiation is enough.

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?

It states it runs 'BEFORE any coding task' and mentions it gathers context for downstream agents, implying it's a prerequisite step. However, it doesn't explicitly compare to alternatives like deep_research_agent or research_scout, but the general usage context is clear.

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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