Mellow Tapir Radio Spectrum Guide

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:

1. Build or refresh the SQLite catalog.
2. Start Ollama and pull gpt-oss:20b.
3. Start the MCP server over HTTP.
4. 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:

1. Edit src/tapir_schema/seed.json.
2. Optionally update src/tapir_schema/schema.sql if source-schema intent changed.
3. 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

1. 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.