Skip to main content
Glama
VelixarAi

Velixar MCP Server

Official
by VelixarAi

velixar_search

Search stored memories by semantic similarity. Filter results by tags, date range, tier, and memory type for precise retrieval.

Instructions

Search stored memories by semantic similarity. Returns ranked results with relevance scores. Supports filtering by tags, date range, tier, and memory type.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNoFilter by tags (AND logic — memory must have all specified tags)
tierNoFilter by memory tier (0=pinned, 1=session, 2=semantic, 3=org)
afterNoISO timestamp — only return memories created after this time
limitNoMax results (default 5)
queryYesSearch query
beforeNoISO timestamp — only return memories created before this time
memory_typeNoFilter by memory type
full_contentNoReassemble chunked memories into full content (default: false). Use for poems, essays, or content that must be returned whole.
Behavior3/5

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

No annotations are present, so the description must convey behavioral traits. It describes reading behavior (search, filtering) and implies no side effects, but does not explicitly state that the tool is read-only or safe. It adequately covers the basic behavior but lacks depth (e.g., no mention of performance, auth requirements, or effect on system state).

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 extremely concise: two sentences that front-load the core functionality and then list filter options. Every word earns its place; there is no irrelevant information or redundancy.

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 8 parameters, no output schema, and many sibling tools, the description covers the essential aspects: search method, filtering, and result ranking. However, it lacks details about the output structure (e.g., fields returned, pagination) and could more explicitly differentiate from similar sibling tools. Nonetheless, it is largely complete for a search tool.

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%, so the baseline is 3. The description summarizes the filtering capabilities (tags, date range, tier, memory type) but does not add meaningful information beyond what the schema already provides for each parameter. No additional constraints or interactions between parameters are explained.

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 function: 'Search stored memories by semantic similarity.' It specifies the resource (memories), the operation (search), and the method (semantic similarity). It also mentions that it returns ranked results with relevance scores, distinguishing it from list or exact-match tools.

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 use for semantic search and mentions ranking, but does not explicitly state when to use this over alternatives like velixar_list, velixar_graph_search, or velixar_multi_search. No when-not or exclusion criteria are provided, leaving the agent to infer usage context.

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/VelixarAi/velixar-mcp-server'

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