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Mission Agent (autonomous missions)

mission_agent

Turn an open-ended growth, revenue, or operations goal into an executed mission. Plan research, prospect, verify, and deliver outbound while retaining memory via thread id for follow-ups.

Instructions

Give AstroFabric an open-ended objective across growth, revenue or digital operations in plain language and it plans and executes the whole mission autonomously: research markets, audit sites, analyze competitors, build prospect lists from buyer intent and signals, verify emails, draft outbound, produce creative, run data work in the code sandbox, deliver into connected apps, and more. The result starts with a [thread:] line - pass that id as thread_id on follow-ups ("verify those emails", "format them for LinkedIn Ads") and the agent remembers everything already asked and delivered. It may reply with a clarifying question; answer it the same way. Prefer this over the individual tools for anything multi-step.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
objectiveYesWhat to accomplish, e.g. "Build a list of 100 companies showing high interest in building insulation"
thread_idNoThe thread id from a previous result, to continue that mission with full memory
Behavior5/5

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

The description goes far beyond the annotations by explaining autonomous planning/execution, the [thread:<id>] result prefix, memory across follow-ups, and the possibility of a clarifying question. It also mentions capabilities like running data work in the code sandbox and delivering into connected apps, which gives an agent a concrete sense of side effects. No contradiction with annotations was mentioned.

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 dense but every sentence adds new information: core functionality, key output format, continuation pattern, clarification behavior, and usage preference. The front-loaded sentence explains scope first, followed by operational details. It is aligned with the best vertex for high-functioning agents.

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

Completeness5/5

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

Given the tool is an autonomous agent with no sibling tools and no output schema, the description surprisingly covers everything essential: what the objective should be, what outcomes can be expected, how to start, how to continue, how to handle clarifying questions, and when to prefer it. The emptiness of a formal output schema is compensated by the explicit mention of the thread line at the beginning. This is a high-quality, self-sufficient description.

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?

The input schema already covers 100% of parameters with meaningful descriptions and an example for objective. The description reinforces thread_id usage in the continuation context but does not materially add new semantic details beyond the schema. This matches the baseline where the schema carries the parameter-load.

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 that the tool takes a plain-language objective and autonomously plans/executes a multi-step mission across growth, revenue, and digital operations. It lists concrete actions (research, audits, competitor analysis, email verification) and explicitly contrasts itself with individual tools. This goes well beyond a vague restatement of the name.

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

Usage Guidelines5/5

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

It explicitly says 'Prefer this over the individual tools for anything multi-step', which specifies when to use it and strongly implies when not to use it (single-step operations). It also documents the follow-up workflow using thread_id and how to respond to clarifying questions. This is strong, actionable usage direction.

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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