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Zilliz MCP Server

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by zilliztech

query

Filter and retrieve data from a Milvus vector database collection using boolean expressions to find specific records based on defined criteria.

Instructions

Conduct a filtering on the scalar field with a specified boolean expression.

Args:
    cluster_id: ID of the cluster
    region_id: ID of the cloud region hosting the cluster
    endpoint: The cluster endpoint URL. Can be obtained by calling describe_cluster and using the connect_address field
    collection_name: The name of the collection to which this operation applies
    filter: The filter used to find matches for the search
    db_name: The name of the database. Pass explicit dbName or leave empty when cluster is free or serverless
    output_fields: An array of fields to return along with the query results
    partition_names: The name of the partitions to which this operation applies. If not set, the operation applies to all partitions in the collection
    limit: The total number of entities to return (default: 100). The sum of this value and offset should be less than 16,384
    offset: The number of records to skip in the search result. The sum of this value and limit should be less than 16,384
Returns:
    Dict containing the query results
    Example:
    {
        "code": 0,
        "cost": 0,
        "data": [
            {
                "color": "red_7025",
                "id": 1
            },
            {
                "color": "red_4794",
                "id": 4
            },
            {
                "color": "red_9392",
                "id": 6
            }
        ]
    }
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cluster_idYes
region_idYes
endpointYes
collection_nameYes
filterYes
db_nameNo
output_fieldsNo
partition_namesNo
limitNo
offsetNo
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds some context, such as default values for limit and offset, constraints on their sum (<16,384), and an example return structure. However, it lacks details on permissions, rate limits, error handling, or whether it's read-only/destructive, which are critical for a query tool with many parameters.

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

Conciseness3/5

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

The description is front-loaded with a purpose statement and organized into Args and Returns sections, but it's verbose with repetitive parameter details. Some sentences, like the filter explanation, could be more concise. The structure is clear but not optimally efficient, with room for trimming without losing value.

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 the complexity (10 parameters, no annotations, no output schema), the description is moderately complete. It covers parameters well and includes an example return, but lacks behavioral details like error cases, performance implications, or how it differs from sibling tools. This leaves gaps for an agent to fully understand the tool's context and usage.

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?

The schema description coverage is 0%, so the description must compensate. It provides detailed explanations for all 10 parameters, including purposes, defaults, and constraints (e.g., limit+offset <16,384), adding significant meaning beyond the bare schema. This effectively documents the parameters, though some nuances like filter syntax could be more explicit.

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

Purpose3/5

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

The description states 'Conduct a filtering on the scalar field with a specified boolean expression,' which vaguely indicates a filtering/query operation but lacks specificity about what resource is being queried (e.g., database collection). It doesn't clearly distinguish from siblings like 'search' or 'hybrid_search,' leaving ambiguity about when to use this tool versus others.

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 'search' or 'hybrid_search.' The description mentions obtaining the endpoint from 'describe_cluster,' implying a prerequisite, but doesn't clarify the tool's specific use case or exclusions relative to siblings, leaving the agent without clear usage context.

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

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