threadline-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| THREADLINE_API_KEY | Yes | Your Threadline API key, obtained from threadline.to/dashboard. |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| injectC | Inject user context into a base system prompt before an LLM call. Returns an enriched prompt with relevant facts about the user inserted automatically. |
| updateC | Update a user's context after an LLM interaction. Threadline extracts structured facts from the conversation and stores them for future sessions. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: 'inject' handles pre-LLM call context insertion, while 'update' handles post-interaction context extraction and storage. There is no overlap in functionality, making them easily distinguishable for an agent.
Both tools use simple, imperative verb names ('inject' and 'update') that are consistent in style and length. This follows a predictable pattern without any deviations or mixed conventions.
With only 2 tools, the server feels thin for its apparent purpose of managing user context across LLM interactions. A typical context management system might benefit from additional operations like retrieving or deleting context, making this set under-scoped.
The tools cover the core workflow of injecting and updating user context, but there are notable gaps. For example, there is no tool to retrieve stored context for review or delete outdated context, which could limit agent functionality in multi-session scenarios.