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yoloshii

gigaxity-deep-research

by yoloshii
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
RESEARCH_LLM_MODELNoModel identifier for the synthesis LLMqwen/qwen3-30b-a3b-thinking-2507
RESEARCH_LLM_API_KEYYesYour OpenRouter API key (or local LLM API key)
RESEARCH_LLM_API_BASENoBase URL for the LLM API endpointhttps://openrouter.ai/api/v1
RESEARCH_SEARXNG_HOSTNoURL of the SearXNG instance (default may be set in .env)

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": true
}
logging
{}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
extensions
{
  "io.modelcontextprotocol/ui": {}
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
searchA

Multi-source search with RRF (Reciprocal Rank Fusion).

Returns ranked results from SearXNG, Tavily, and LinkUp. Use for raw search results without synthesis. No LLM call.

researchA

Full research pipeline: search + LLM synthesis with citations.

Pipeline: Multi-source search → Source aggregation → LLM synthesis → Citation formatting

askA

Quick conversational answer using LLM.

No search, direct response from model knowledge. Use for simple factual questions or follow-ups.

discoverA

Exploratory discovery with knowledge gap analysis.

Identifies what's known and unknown about a topic. Use for cold-start exploration.

synthesizeA

Synthesize pre-gathered content into coherent analysis.

Use when you already have sources from other tools.

reasonA

Deep reasoning with chain-of-thought analysis.

Two modes, picked automatically by whether sources is provided:

  • No-sources mode (default): direct chain-of-thought over the model's own knowledge plus optional context. Use for problems the model can reason about without external evidence; depth-controlled via reasoning_depth.

  • Sources-aware mode (when sources is non-empty): chain-of-thought synthesis over the pre-gathered sources, with the same shape REST /api/v1/reason produces. reasoning_depth is ignored in this mode — the chain-of-thought prompt is fixed because the reasoning shape is what matters here, not the prose register.

For style variants over pre-gathered sources, call synthesize directly.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.2/5.0

Scored across 6 tools

Disambiguation5/5

Each tool has a distinct purpose: ask for quick answers, discover for exploration, reason for chain-of-thought, research for full pipeline, search for raw results, and synthesize for post-gathering analysis. No significant overlap.

Naming Consistency5/5

All tool names are single, lowercase verbs: ask, discover, reason, research, search, synthesize. Very consistent pattern.

Tool Count5/5

6 tools is ideal for a research server, covering all key operations without being excessive or sparse.

Completeness5/5

The set covers the full research workflow from exploration to synthesis, including raw search, quick answers, deep reasoning, and full pipeline. No obvious gaps.

Maintenance

ActivityActive
ResponsivenessUnresponsive

Resources