Skip to main content
Glama

Metis · Memory Curator — Search Memory

search_memory

Find past context from notes, decisions, and journal entries by searching with keywords or phrases, optionally filtered by type.

Instructions

Search the memory palace by keyword.

Looks across Metis's long-term memory to recall past context — what was
decided, found, or noted before. It searches the memory_entries table
(title, summary, topics) and also greps journal/**/*.md files on disk, so
both structured memory and freeform journal notes are covered.

Args:
    query: Keyword or phrase to match against entry titles, summaries,
        topics, and journal note text.
    entry_type: Optional filter limiting results to one kind of entry —
        "session", "journal", "idea", "decision", or "topic". Empty string
        (default) searches all types.

Returns:
    A text block of matching memory entries and journal hits, or a message
    when nothing matches.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
entry_typeNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations exist, so the description must fully disclose behavior. It describes search sources and return format but lacks details on idempotency, permissions, or side effects. As a read-only search, it is acceptable but not explicit about safety.

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

Conciseness4/5

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

The description is well-structured with clear sections (Args, Returns). It is concise while providing necessary details, though it could be slightly tighter.

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 2 non-enum parameters and an output schema, the description adequately explains the search sources, fields covered, and return format. No critical gaps for a simple search tool.

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 has no descriptions (0% coverage). The description adds meaning: query is a keyword or phrase, entry_type lists possible values (session, journal, idea, decision, topic) and default behavior. This compensates well for the schema gap.

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 it searches memory by keyword across both structured entries and journal notes, with explicit scope (memory_entries table and journal files). This is specific and distinct from sibling tools like search_session_memory or search_library.

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 explains what data is searched and mentions optional filtering by entry type. However, it does not explicitly contrast with other search tools or provide when-not-to-use guidance, though the scope is clear enough for basic usage.

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