Skip to main content
Glama

rememb_search

Search memory entries by content or tags using semantic similarity to find relevant information from stored data.

Instructions

Search memory entries by content or tags using semantic similarity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSearch query - natural language or keywords
top_kNoMaximum number of results
Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions 'semantic similarity' as the search method, which adds some behavioral context beyond basic search. However, it lacks details on permissions, rate limits, output format, or error handling. For a search tool with zero annotation coverage, this is a significant gap in transparency.

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 a single, efficient sentence with zero waste—it directly states the tool's purpose and method. It's appropriately sized and front-loaded, making it easy to parse quickly without unnecessary elaboration.

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

Completeness3/5

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

Given 2 parameters with full schema coverage and no output schema, the description is adequate but incomplete. It covers the basic purpose and method but lacks details on behavioral aspects like permissions or output structure. For a search tool with no annotations, it should provide more context to be fully helpful, but it meets minimum viability.

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%, with clear descriptions for both parameters (query as 'natural language or keywords', top_k as 'maximum number of results'). The description adds minimal value beyond the schema, mentioning 'content or tags' which relates to the query parameter but doesn't specify syntax or format. Baseline 3 is appropriate as the schema does the heavy lifting.

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

Purpose4/5

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

The description clearly states the action ('Search') and target resource ('memory entries'), specifying search criteria ('by content or tags using semantic similarity'). It distinguishes from siblings like rememb_read (likely direct retrieval) and rememb_write (creation), but doesn't explicitly contrast them. Purpose is specific but sibling differentiation is implied rather than explicit.

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

Usage Guidelines3/5

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

The description implies usage context—searching when you have content or tags to match—but doesn't explicitly state when to use this versus alternatives like rememb_read (which might retrieve by ID) or rememb_edit (modification). No guidance on prerequisites, exclusions, or named alternatives is provided, leaving usage context partially inferred.

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/LuizEduPP/Rememb'

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