Skip to main content
Glama

Metis — Update Project Memory

update_project_memory

Append a session summary to a project's history and refresh its prompt memory. Future sessions will automatically know what occurred before.

Instructions

Append a session summary to a project's history and refresh its prompt memory.

Call this at the end of any work session on a project. The history feeds
into load_project_context() so future sessions automatically know what
happened before.

Args:
    project_id: The project slug.
    what_was_done: 1-3 sentence summary of what was accomplished this session.
    next_steps: Optional: what needs to happen next. Updates the next_step field.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
next_stepsNo
project_idYes
what_was_doneYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description must fully disclose behavior. It states it appends to history and updates prompt memory and next_steps. However, it does not explain what 'refresh prompt memory' entails or whether the operation is idempotent, leaving some behavioral ambiguity.

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 concise, with no redundant sentences. It uses a clear structure: purpose, usage instruction, then parameter list. The front-loaded purpose immediately conveys the tool's intent.

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?

Given the tool has 3 parameters and an output schema exists, the description covers the core functionality, usage, and parameter semantics adequately. It lacks details on return values but the output schema fills that gap. Overall, it is sufficiently complete for an AI agent.

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

Parameters4/5

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

Schema description coverage is 0%, so compensation is needed. The description explains 'what_was_done' as a 1-3 sentence summary and 'next_steps' as optional, updating the next_step field. 'project_id' is described as 'The project slug,' which is sufficient. This adds meaning beyond the schema's titles and defaults.

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's action: 'Append a session summary to a project's history and refresh its prompt memory.' The verb 'append' and resource 'project history' are specific. Among memory-related siblings, the focus on session summaries at session end differentiates it.

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 explicitly says 'Call this at the end of any work session on a project,' providing clear when-to-use guidance. It also explains the benefit (feeds into load_project_context), though it does not discuss when not to use or list alternatives.

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/SVerITG/Metis'

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