Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@mcp-server-coordinate Extract commitments from: 'Alice will send the proposal by Thursday.'"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
An MCP server that extracts structured commitments from unstructured text meeting transcripts, email threads, Slack logs, or SMS chains.
Given messy human communication, it tells you: who promised what, by when, and under what conditions.
"Sarah said she'd loop in the design team once the spec is finalized,
and Tom committed to shipping the API endpoint by end of sprint."↓
Commitment 1
Person: Sarah
Committed: Loop in the design team
Deadline: not stated
Conditions: once the spec is finalized
Confidence: high
Quote: "she'd loop in the design team once the spec is finalized"
Commitment 2
Person: Tom
Committed: Ship the API endpoint
Deadline: end of sprint
Conditions: none
Confidence: high
Quote: "committed to shipping the API endpoint by end of sprint"npm install -g mcp-server-coordinateOr run without installing:
npx mcp-server-coordinate --file transcript.txtRelated MCP server: actions-xyz-mcp
An Anthropic API key:
export ANTHROPIC_API_KEY=sk-ant-...Add to your MCP config:
{
"mcpServers": {
"coordinate": {
"command": "npx",
"args": ["-y", "mcp-server-coordinate"],
"env": {
"ANTHROPIC_API_KEY": "sk-ant-..."
}
}
}
}Then in Claude, use the extract_commitments tool:
Extract commitments from this transcript: [paste text]
# Inline text
mcp-server-coordinate "Alice will send the proposal by Thursday."
# From a file
mcp-server-coordinate --file meeting.txt
# Pipe from stdin
cat thread.txt | mcp-server-coordinateEach commitment includes:
Field | Description |
| Who made the commitment |
| What they agreed to do |
| When (verbatim from source, e.g. "Friday", "end of Q2") |
| Any stated conditions ("if budget is approved") |
|
|
| The shortest verbatim excerpt proving the commitment |
The response also includes source_type (transcript / email / sms / thread / unknown) and estimated participant_count.
Uses claude-haiku-4-5 with tool use / structured output. The model is prompted to extract commitments conservatively — it distinguishes between strong commitments ("I'll send it by Friday") and weak ones ("I'll look into it"), and surfaces that distinction in the confidence field.
extract_commitments
Parameter | Type | Description |
|
| The unstructured text to analyze |
MIT
This server cannot be deployed
Extract structured data points from research papers and other documents with an LLM.
Turn messy text into strict JSON schemas agents can trust (invoice, receipt, contact, resume).
Turn sales-call transcripts into traced proposals, contracts, and NDAs.
Ask Claude about your meetings: summaries, action items and transcripts from Roomtone
