Mellow Tapir Radio Spectrum Guide

Name: Mellow Tapir Radio Spectrum Guide
Author: guycole

by guycoleversion 1.0.2i★ –iRuns on your computer

Registry nameio.github.guycole/mellow-tapir

Install

Overview

Description from the maker (original English)

Explore likely radio services, channels, and emissions from 108 MHz to 1 GHz.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: Streamable HTTP
Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

DockerDocker installed

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "mellow-tapir": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/guycole/mellow-tapir-mcp:1.0.2"
      ]
    }
  }
}

Run this in your terminal

claude mcp add mellow-tapir -- docker run -i --rm ghcr.io/guycole/mellow-tapir-mcp:1.0.2

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "mellow-tapir": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/guycole/mellow-tapir-mcp:1.0.2"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "mellow-tapir": {
      "type": "stdio",
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/guycole/mellow-tapir-mcp:1.0.2"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account guycole, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

More stars than 0% of the other 32599 servers in this category
Published to the registry more recently than 47% of the other 32599 servers in this category
Basic checks equal to or better than 100% of the other 32599 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Mellow Tapir Radio Spectrum Guide safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.0.2) reached the registry 2 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Docker on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, Zendesk

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Spec Driven Development

by formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

Runs on your computer★ 440MITUpdated 14 months ago

Install

Remote Desktop Commander

by desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

Runs in the cloud★ 211Updated 2 days ago

Install

Zendesk

by pipeworx-io

Zendesk MCP Pack — tickets, users, organizations via OAuth.

Computer or cloudUpdated today

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Tapir MCP Server

This directory contains an MCP server with both stdio and streamable HTTP transports. The server uses data from src/tapir_schema/seed.json and source schema text from src/tapir_schema/schema.sql, then serves a read-only SQLite catalog.

Python environment

Use the existing local virtual environment:

cd src/tapir_mcp_server

./venv/bin/python -m pip install -r requirements.txt

Build the SQLite database

cd /Users/gsc/github/mellow-tapir/src/tapir_mcp_server

./venv/bin/python -m mcp_server.build_db \ --db-path data/tapir.sqlite \ --seed-path ../tapir_schema/seed.json \ --schema-path ../tapir_schema/schema.sql

Run in stdio mode

cd /Users/gsc/github/mellow-tapir/src/tapir_mcp_server

TAPIR_DB_PATH=data/tapir.sqlite \ TAPIR_SEED_PATH=../tapir_schema/seed.json \ TAPIR_SCHEMA_PATH=../tapir_schema/schema.sql \ ./venv/bin/python -m mcp_server --transport stdio

Run in HTTP mode

cd /Users/gsc/github/mellow-tapir/src/tapir_mcp_server

TAPIR_DB_PATH=data/tapir.sqlite \ TAPIR_SEED_PATH=../tapir_schema/seed.json \ TAPIR_SCHEMA_PATH=../tapir_schema/schema.sql \ REFERENCE_LLM_MODEL=gpt-oss:20b \ OLLAMA_BASE_URL=http://127.0.0.1:11434 \ ./venv/bin/python -m mcp_server \ --transport http --host 0.0.0.0 --port 8000 --path /mcp

Local testing with Ollama gpt-oss:20b

Start Ollama and ensure the reference model is available:

ollama serve

In another terminal:

ollama pull gpt-oss:20b

ollama run gpt-oss:20b "Summarize the purpose of this MCP server in one sentence."

Run the MCP server with the same local Ollama endpoint:

cd /Users/gsc/github/mellow-tapir/src/tapir_mcp_server

TAPIR_DB_PATH=data/tapir.sqlite \ TAPIR_SEED_PATH=../tapir_schema/seed.json \ TAPIR_SCHEMA_PATH=../tapir_schema/schema.sql \ REFERENCE_LLM_MODEL=gpt-oss:20b \ OLLAMA_BASE_URL=http://127.0.0.1:11434 \ ./venv/bin/python -m mcp_server --transport http --host 127.0.0.1 --port 8000 --path /mcp

When connected with an MCP client, call ask_reference_model with a test prompt. If Ollama is reachable and the model is present, the tool returns a response body and metadata including model and done status.

Sample local MCP session (with Ollama)

This sample session verifies end-to-end behavior:

Build or refresh the SQLite catalog.
Start Ollama and pull gpt-oss:20b.
Start the MCP server over HTTP.
Connect with a client and call tools.

Terminal 1: start Ollama

ollama serve

Terminal 2: pull model and optional sanity prompt

ollama pull gpt-oss:20b

ollama run gpt-oss:20b "Reply with: Ollama ready"

Terminal 3: start Tapir MCP server

cd /Users/gsc/github/mellow-tapir/src/tapir_mcp_server

./venv/bin/python -m mcp_server.build_db \ --db-path data/tapir.sqlite \ --seed-path ../tapir_schema/seed.json \ --schema-path ../tapir_schema/schema.sql

TAPIR_DB_PATH=data/tapir.sqlite \ TAPIR_SEED_PATH=../tapir_schema/seed.json \ TAPIR_SCHEMA_PATH=../tapir_schema/schema.sql \ REFERENCE_LLM_MODEL=gpt-oss:20b \ OLLAMA_BASE_URL=http://127.0.0.1:11434 \ ./venv/bin/python -m mcp_server --transport http --host 127.0.0.1 --port 8000 --path /mcp

Terminal 4: sample client requests

cd /Users/gsc/github/mellow-tapir

/Users/gsc/github/mellow-tapir/src/tapir_mcp_server/venv/bin/python - <<'PY' import anyio from mcp.client.session import ClientSession from mcp.client.streamable_http import streamable_http_client

async def main() -> None: async with streamable_http_client("http://127.0.0.1:8000/mcp") as ( read_stream, write_stream, ): session = ClientSession(read_stream, write_stream) async with session: await session.initialize()

classify = await session.call_tool( "classify_frequency", {"frequency": "121.5"} ) print("classify_frequency:") print(classify.content[0].text)

llm = await session.call_tool( "ask_reference_model", { "prompt": "Explain 121.5 MHz in one sentence.", "temperature": 0.1, }, ) print("ask_reference_model:") print(llm.content[0].text)

anyio.run(main) PY

Expected outcome:

classify_frequency returns aviation emergency channel data around 121.5 MHz.
ask_reference_model returns a completion from local Ollama model gpt-oss:20b.

Available tools

classify_frequency
Purpose: classify a single frequency into matching bands and exact known channels.
Input: frequency as numeric Hz, MHz string (121.5), or explicit unit (121.5MHz).
Output: frequency metadata, channel matches, and band matches with emissions.
query_channels
Purpose: list known channels in a frequency range.
Input: lower_hz, upper_hz, optional limit.
Output: ordered channel rows with labels, services, and emissions.
search_services
Purpose: text search across service names and use names.
Input: service_query, optional limit.
Output: matching service/use combinations with range summaries.
execute_readonly_sql
Purpose: ad hoc read-only analysis over the SQLite catalog.
Input: SQL SELECT or WITH query and optional limit.
Output: column list, row list, and row count.
Guardrails: write operations are rejected.
ask_reference_model
Purpose: query local reference LLM through Ollama.
Input: prompt, optional system_prompt, optional temperature.
Output: model response text and completion metadata.
Default model: gpt-oss:20b (override with REFERENCE_LLM_MODEL).

Available resources

tapir://metadata
tapir://sources/seed-json
tapir://sources/schema-sql

Docker

Build locally:

cd /Users/gsc/github/mellow-tapir

docker build -f src/tapir_mcp_server/Dockerfile -t tapir-mcp:local .

Run:

docker run --rm -p 8080:8080 \

-e OLLAMA_BASE_URL=http://host.docker.internal:11434 \ tapir-mcp:local

The GitHub Actions workflow in .github/workflows/tapir-mcp-server-image.yml publishes images to GitHub Container Registry.

SQLite data lineage and refresh

The SQLite database in data/tapir.sqlite is generated from two source files:

src/tapir_schema/seed.json: canonical editable catalog of bands, emissions, and channels.
src/tapir_schema/schema.sql: source PostgreSQL schema retained for provenance and parity checks.

Generation is handled by mcp_server.build_db, which:

creates SQLite tables (band, band_emission, channel, dataset_metadata),
reloads records from seed.json,
stores source provenance metadata (including source paths and schema SQL text),
produces a read-ready file used in read-only mode by the MCP server.

When updating the dataset in the future:

Edit src/tapir_schema/seed.json.
Optionally update src/tapir_schema/schema.sql if source-schema intent changed.
Rebuild SQLite:

cd /Users/gsc/github/mellow-tapir/src/tapir_mcp_server

./venv/bin/python -m mcp_server.build_db \ --db-path data/tapir.sqlite \ --seed-path ../tapir_schema/seed.json \ --schema-path ../tapir_schema/schema.sql

Restart the MCP server so it uses the refreshed SQLite file.

The container image build also runs this same build step during docker build, so image contents stay aligned with the latest checked-in seed.json and schema.sql.

Mellow Tapir Radio Spectrum Guide

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Otheri

Common questions

Alternatives

Spec Driven Development

Remote Desktop Commander

Zendesk

README from the maker

Tapir MCP Server

Python environment

Build the SQLite database

Run in stdio mode

Run in HTTP mode

Local testing with Ollama gpt-oss:20b

Sample local MCP session (with Ollama)

Available tools

Available resources

Docker

SQLite data lineage and refresh