mcp-server-coordinate

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"

Installation

npm install -g mcp-server-coordinate

Or run without installing:

npx mcp-server-coordinate --file transcript.txt

Requirements

An Anthropic API key:

export ANTHROPIC_API_KEY=sk-ant-...

Usage

As an MCP server (Claude Desktop / Cursor / etc.)

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]

As a CLI

# 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-coordinate

What gets extracted

Each commitment includes:

Field	Description
person	Who made the commitment
commitment	What they agreed to do
deadline	When (verbatim from source, e.g. "Friday", "end of Q2")
conditions	Any stated conditions ("if budget is approved")
confidence	high / medium / low
source_quote	The shortest verbatim excerpt proving the commitment

The response also includes source_type (transcript / email / sms / thread / unknown) and estimated participant_count.

How it works

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.

MCP Tool

extract_commitments

Parameter	Type	Description
text	string	The unstructured text to analyze

License

MIT