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MCP Ollama Consult Server

MCP Ollama Consult Server

by Atomic-Germ
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Hybrid

MCP Ollama Consult Server

TypeScript MCP CI/CD Ollama Consult Server MCP

An MCP (Model Context Protocol) server that allows consulting with Ollama models for reasoning from alternative viewpoints.

Features

  • consult_ollama: Send prompts to Ollama models and get responses

  • list_ollama_models: List available models on the local Ollama instance

  • compare_ollama_models: Run the ame prompt against multiple Ollama models and return their outputs side-by-side for comparison

  • remember_consult: Store the result of a consult into a local memory store (or configured memory service)

Related MCP server: MCP Ollama

Installation

  1. Install the server:

    npm i -g https://github.com/Atomic-Germ/mcp-consult/releases/download/v1.0.1/mcp-ollama-consult-1.0.1.tgz

  2. Configure the server:

    {
 "servers": {
     "ollama-consult": {
         "type": "stdio",
         "command": "mcp-ollama-consult",
         "args": []
     }
 },
 "inputs": []
}

Usage

Make sure Ollama is running locally (default: (http://localhost:11434).

Start the MCP server:

mcp-ollama-consult

Or for development:

npm run dev

Configuration

Set the OLLAMA_BASE_URL environment variable to change the Ollama endpoint:

OLLAMA_BASE_URL=http://your-ollama-server:11434 npm start

Docker

To run with Docker, build the image:

FROM node:18-alpine
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production
COPY dist/ ./dist/
CMD ["node", "dist/index.js"]

Requirements

  • Node.js 18+

  • Ollama running locally or accessible via HTTP

Available Tools

5 tools
compare_ollama_responsesB

Compare responses from multiple Ollama models on the same prompt to get diverse perspectives

Inputs: prompt (required), models, context.

consult_ollamaB

Consult with Ollama AI models for architectural decisions, code reviews, and design discussions. Supports sequential chaining of consultations for complex multi-step reasoning.

Inputs: prompt (required), consultation_type, model, system_prompt, +4 more.

list_ollama_modelsA

List all available Ollama models on the local system (installed or cloud-based)

Inputs: no named parameters.

remember_contextB

Store context for use in future consultations within the same session

Inputs: key (required), value (required), metadata.

sequential_consultation_chainB

Run a sequence of consultations where each consultant builds on previous responses, enabling complex multi-step reasoning.

Inputs: consultants (required), context, flowControl, memory.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    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

    B3.4/5.0

    Scored across 5 tools

    Disambiguation3/5

    The purposes of list_ollama_models and remember_context are distinct, but consult_ollama, compare_ollama_responses, and sequential_consultation_chain heavily overlap. All three involve querying models, differing mainly in whether responses are combined or chained, which could lead to agent confusion.

    Naming Consistency3/5

    Tools mostly follow a snake_case verb_noun pattern (list_ollama_models, remember_context, compare_ollama_responses), but 'sequential_consultation_chain' breaks the verb-first pattern with an adjective-noun structure. Also, 'consult_ollama' is a general verb that overlaps with others, making the naming less distinctive.

    Tool Count5/5

    With only 5 tools, the server is well-scoped and each tool seems to have its place, though the overlap among core consultation tools slightly reduces the necessity of having three separate ones. Still, it's a small, manageable set that fits the server's purpose.

    Completeness4/5

    The server covers a typical consultation workflow: listing available models, querying them, comparing responses, chaining consultations, and maintaining context. A minor gap is the lack of a tool to explicitly clear context or manage sessions, which agents might need for long-running interactions.

    Maintenance

    ActivityInactive
    ResponsivenessNo recent community issues

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