Skip to main content
Glama

extract_commitments

Parse meeting transcripts to identify commitments, capturing who promised what and by when. Automatically create store records and Linear tickets while deduplicating against existing entries.

Instructions

Parses a meeting transcript and returns structured commitment objects: who promised what, to whom, by when, and with what confidence. For every commitment found it immediately creates a durable store record AND a Linear ticket — catching the 90% of commitments that would never become a manual ticket. Deduplicates against commitments already in the store.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
meeting_dateYesMeeting date as YYYY-MM-DD — used to resolve relative deadlines like "by Friday"
participantsNoOptional roster to resolve owners against
transcript_idYesStable id for this meeting
transcript_textYesFull meeting transcript, one "Speaker: statement" per line
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden of disclosing side effects. It explicitly states that it immediately creates a durable store record AND a Linear ticket, and that it deduplicates against existing commitments. This is key behavioral information, though it could add details on idempotency or failure handling.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences, front-loaded with the core purpose, then side effects, then dedup. No wasted words; every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite having no output schema and no annotations, the description explains what the tool returns, its side effects, and its dedup behavior. It does not cover potential edge cases or the exact output format, but for a tool with 4 parameters and clear side effects, it is largely complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description does not add significant parameter-specific details beyond what the schema already provides, but it does frame the overall purpose (transcript parsing) which indirectly applies to parameters like transcript_text and meeting_date.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool parses a meeting transcript and returns structured commitment objects, with a specific verb and resource. It also distinguishes itself from siblings like upsert_commitment and query_commitments by emphasizing the extraction and automatic ticket creation behavior.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for meeting transcripts and mentions catching commitments that would otherwise be missed, providing clear context. It does not explicitly name alternative tools or exclusions, but the context is sufficient for an agent to infer when to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/ionfwsrijan/FollowThrough'

If you have feedback or need assistance with the MCP directory API, please join our Discord server