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agno_integrations

Retrieve integration documentation for databases, vector stores, and models. Specify the integration type and optional name to get targeted guides.

Instructions

Get integration documentation for databases, vector stores, and models.

Args: integration_type: Type of integration. One of: database, vectordb, models, toolkits name: Specific integration name. Leave empty to list all of that type. Database: postgres, mongodb, sqlite, mysql, redis, dynamodb, firestore VectorDB: pinecone, qdrant, chroma, weaviate, milvus, lancedb Models: openai, anthropic, google, azure, bedrock

Examples: - integration_type="database", name="postgres" - integration_type="vectordb", name="pinecone" - integration_type="models" (lists all providers)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNo
integration_typeYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
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 only describes basic functionality and valid parameter values, but lacks disclosure of authentication needs, rate limits, error handling, or potential side effects. This is minimal for a read tool.

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 concise: a brief purpose statement, followed by well-structured Args and Examples sections. Every sentence adds value with no 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?

The description covers the input parameters sufficiently with valid values and examples. However, it does not describe the output format or what the returned documentation looks like, though an output schema is present (not shown). Slightly incomplete without output details.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema coverage, the description adds significant meaning: it explains the integration_type enum, provides specific name values, and gives examples. This fully compensates for the missing schema descriptions.

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 'gets integration documentation for databases, vector stores, and models,' specifying the verb and resource. It lists integration types and examples, distinguishing it from sibling tools like agno_agentos or agno_api.

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 provides clear guidance on parameter values (e.g., integration_type options, name options) and examples. However, it does not explicitly state when to use this tool over siblings or when not to use it, but the context is self-explanatory.

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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