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ecosystem_recipes

Lists integration recipes to combine external MCP servers with AI Team OS workflows, including setup and collaboration scenarios.

Instructions

List available ecosystem integration recipes for combining AI Team OS with external tools.

Recipes describe how to integrate external MCP servers (GitHub, Slack, Linear, etc.) with AI Team OS workflows. Each recipe includes: recommended MCP server, install config, and concrete collaboration scenarios with AI Team OS tools.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
recipe_idNoOptional recipe identifier to get details for a specific recipe. Leave empty to list all available recipes. Valid IDs: "github", "slack", "linear", "fullstack-team".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

No annotations are provided, so the description must stand alone. It discloses that the tool lists recipes with MCP server, config, and scenarios, which is sufficient for a read-only list operation. However, it does not mention any behavioral traits like authentication requirements or rate limits.

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 two sentences: the first states the core purpose, the second elaborates on what recipes include. Every sentence adds value with no redundancy or verbosity.

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 the presence of an output schema (indicated by context signals), the description does not need to detail return values. It provides adequate information about what the tool does and the contents of recipes, making it sufficiently complete for a listing tool.

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 100%, and the description adds value by explaining that leaving the optional parameter empty lists all recipes, while providing a valid ID retrieves details. This clarifies the parameter's behavior beyond the schema's basic type and description.

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 ('List available ecosystem integration recipes') and the resource ('recipes for combining AI Team OS with external tools'). It also distinguishes from sibling tools by specifying that it provides recipes for integration, which is unique among the many ecosystem_ 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 explains the tool's function but does not explicitly state when to use it versus alternatives. It lacks guidance on when not to use it or preconditions. The optional parameter for specific recipe details is implied but not contextualized with other tools.

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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