Skip to main content
Glama
Sign In
Redis MCP Server

Redis MCP Server

by Yuchenhui
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

set_vector_in_hash

Save a numeric vector as a field in a Redis hash, enabling vector storage for similarity searches.

Instructions

Store a vector as a field in a Redis hash.

Args: name: The Redis hash key. vector_field: The field name inside the hash. Unless specifically required, use the default field name vector: The vector (list of numbers) to store in the hash.

Returns: True if the vector was successfully stored, False otherwise.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "name": "<name>",
      "vector": "<vector>"
    },
    "name": "set_vector_in_hash"
  }
}

Input Schema

NameRequiredDescriptionDefault
nameYes
vectorYes
vector_fieldNovector

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.5

TDQS

B3.2/5.0
Behavior2/5

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

With no annotations, the description must disclose behavior. It only states return type (True/False) but omits side effects (e.g., overwrite behavior, index requirements, what happens if hash doesn't exist).

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?

Very concise, uses clear docstring format with Args/Returns. No redundant information, every sentence adds value.

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

Completeness2/5

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

Given no annotations and 3 parameters, the description is incomplete. It lacks context on prerequisites (e.g., hash existence), error cases, and when to use default vs custom vector_field. Output schema exists but is minimal.

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 coverage is 0%, so description must explain parameters. It does so for name, vector, and vector_field, including default and a usage note. However, lacks details like vector length limits or constraints.

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 action ('Store a vector as a field in a Redis hash'), specifying the verb and resource. It distinguishes from sibling tools like get_vector_from_hash and create_vector_index_hash.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool versus alternatives like hset or vector_search_hash. The only hint is 'Unless specifically required, use the default field name,' which is not enough to guide selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools