Skip to main content
Glama

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
TASK_CONTEXT_MCP__DATA_DIRNoData directory path./data
TASK_CONTEXT_MCP__DATABASE_URLNoDatabase URLsqlite:///./data/task_context.db
TASK_CONTEXT_MCP__LOGGING_LEVELNoLogging levelINFO

Tools

Functions exposed to the LLM to take actions

NameDescription
get_active_task_contexts

Start here for every task.

Lists active task contexts (reusable task TYPES, not task instances).

Next steps:

  • If a context matches: call get_artifacts_for_task_context(task_context_id)

  • If no context matches: call create_task_context(summary, description)

create_task_context

Create a new task context (task type) when no match exists.

Use for categories (e.g., "CV analysis for Python dev"), not specific instances.

Constraints:

  • English only

  • summary <= 200 chars

  • description <= 1000 chars

Next step: create initial guidance with create_artifact() before doing task work.

get_artifacts_for_task_context

Load artifacts for a task context.

Call this after you select or create a task context and before doing work. Re-call when you start a new phase or need to confirm guidance.

Notes:

  • Defaults to practice/rule/prompt (excludes result)

  • Set include_archived=True only when you need historical context

create_artifact

Create a new artifact to capture reusable guidance.

Create immediately when you discover a pattern, constraint, mistake, or useful template. If similar guidance might already exist, call search_artifacts() first; prefer update_artifact() over near-duplicates.

Constraints:

  • English only

  • summary <= 200 chars

  • content <= 4000 chars

  • No PII, no task-instance specifics; focus on WHAT/WHY

Types: practice (guidelines), rule (constraints), prompt (templates), result (generalizable learnings).

update_artifact

Update an artifact when existing guidance is incomplete, wrong, or needs refinement.

Use immediately when you learn something better or user feedback indicates a correction. Prefer updating over creating duplicates.

Constraints:

  • English only

  • summary <= 200 chars

  • content <= 4000 chars

  • No PII, no task-instance specifics; focus on WHAT/WHY

Provide summary and/or content.

archive_artifact

Archive an artifact that is incorrect, misleading, or outdated.

Prefer creating a replacement first, then archive the old artifact. Provide a reason when possible.

search_artifacts

Full-text search across artifacts.

Use this before creating new artifacts to avoid duplicates. Returns results ranked by relevance.

reflect_and_update_artifacts

Reflection checkpoint.

Call before declaring a task complete, and after corrections or user feedback. This returns the current artifacts and prompts you to create/update/archive as needed.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

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/l0kifs/task-context-mcp'

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