Tool Manifest

The core of a tool manifest is a precise definition of the tool's inputs and outputs, typically using a structured schema language like JSON Schema or an OpenAPI operation object. This includes:

• Parameter names and data types (e.g., string, integer, array)
• Validation rules (e.g., required, minLength, enum)
• Descriptions for each field to guide the AI's parameter generation
• The structure of the expected response object This schema enables the AI to construct valid API calls and parse the results correctly.

This section declares how the AI agent must authenticate to use the tool, ensuring secure API execution. It specifies:

• The authentication scheme (e.g., apiKey, OAuth2, bearer)
• The location of credentials (e.g., header, query, cookie)
• Required authorization scopes or permissions
• References to credential identifiers managed by a secure system This metadata allows the orchestration layer to retrieve the correct tokens and apply them to requests without exposing secrets to the model itself.

Human and machine-readable metadata that answers 'what does this tool do?' This includes:

• A clear, natural language name and description of the tool's purpose
• Functional tags or categories (e.g., database, communication, analytics)
• The HTTP method and endpoint path for RESTful tools
• Version information for managing breaking changes This description is critical for tool discovery, allowing an AI agent or developer to search a tool registry for a function that matches a needed capability.

Defines the operational boundaries and requirements for running the tool. Key elements are:

• Rate limiting policies or quotas
• Expected latency or timeout values
• Idempotency keys for safe retries
• Data residency or geographic constraints
• Dependencies on other services or tools These constraints inform the orchestration layer's error handling and retry logic, helping it manage failures and adhere to operational limits.

The manifest contains the concrete information needed to execute a call. This is the bridge from declaration to action. It specifies:

• The base URL or network endpoint for the service
• The exact path to the function
• The serialization format (e.g., JSON, Protobuf)
• For local functions, a reference to the handler or executable code This linkage enables dynamic binding, where the AI agent's abstract request is resolved to a specific network call or local function execution at runtime.

To improve reliability, manifests often include sample inputs and outputs. These serve multiple purposes:

• Few-shot examples for the LLM, demonstrating correct usage
• Unit tests for automated validation of the tool's integration
• Documentation for human developers
• Request/Response Validation suites to ensure the live API conforms to the declared schema These artifacts support Evaluation-Driven Development for the tool-calling system, providing ground truth for verifying agent behavior.

A centralized or decentralized catalog that stores and indexes the metadata from tool manifests. It acts as the system of record for available capabilities, enabling AI agents to query for tools based on function, schema, or tags. Key functions include:

• Version Management: Tracking multiple versions of the same tool.
• Health Status Aggregation: Monitoring tool availability via health check endpoints.
• Namespace Organization: Preventing naming collisions by grouping tools.
• Dynamic Updates: Supporting runtime registration and deregistration of tools.

The automated network process by which AI agents or clients dynamically locate the live network endpoints (IP, port) for a tool after its capabilities have been identified via a registry. This separates what a tool does (in the manifest) from where it runs. Common patterns include:

• Client-Side Discovery: The agent queries a registry (e.g., Consul, etcd) to get a list of available instances.
• Server-Side Discovery: The agent makes a request to a load balancer, which queries the registry and routes the request.
• Protocols: Implemented via standards like DNS-SD (DNS-Based Service Discovery) or mDNS for local networks.

The process of parsing and loading structured API definitions—most commonly OpenAPI Specification (OAS) or JSON Schema documents—to automatically generate or populate a tool manifest. This is a critical automation step for integrating existing enterprise APIs. The ingestion pipeline typically:

• Parses the specification file.
• Extracts function names, endpoint paths, HTTP methods, and parameter schemas.
• Maps these elements to the standardized fields of a tool manifest.
• Validates the schema for completeness before registration.

A standardized set of rules and message formats that defines how a tool or service announces its availability and metadata to a registry. It governs the lifecycle of a tool's entry in the system. Key aspects include:

• Registration Payload: The structure (often based on the tool manifest) sent to the registry.
• Self-Registration: The common pattern where a service autonomously registers itself on startup.
• Lease-Based Registration: Registrations are granted for a limited time (a lease) and must be periodically renewed via heartbeats, or they are automatically cleaned up.
• Deregistration: The formal process for removing an entry during graceful shutdown.

A dedicated API endpoint (e.g., /.well-known/tools or /openapi.json) on a service that, when queried, returns a structured description of its available tools and schemas. This enables dynamic, runtime discovery without pre-loaded manifests. Characteristics include:

• Self-Describing: Allows clients to discover capabilities directly from the running service.
• Standardized Format: Typically returns an OpenAPI document or a manifest-compliant JSON object.
• Used for Discovery: A registry or agent can poll this endpoint to populate its catalog.
• Contrast with Static Manifest: Provides real-time capability reporting, whereas a static manifest is a pre-defined file.

The runtime mechanism that connects an AI agent's abstract request for a tool (e.g., "call a database query function") to a specific implementation and network endpoint. It occurs after discovery and involves:

• Tool Resolution: Selecting one specific implementation from multiple registered options that match the required schema, often based on version, priority, or load.
• Endpoint Resolution: Translating a logical service name into a concrete IP address and port.
• Capability Negotiation: Potentially agreeing on a mutually compatible interface version between client and service.
• Load Balancer Integration: Binding to a healthy instance selected by a load-balancing algorithm.